Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
Authors: Yichen Li
Yijing Shan
YI LIU
Haozhao Wang
Cheng Wang
wangshi.ww
Yi Wang
Ruixuan Li
Venue: NeurIPS 2025 spotlight ()
Keywords: Federated Learning
Recommendation System
Primary area: other

Abstract: The current Federated Recommendation System (FedRS) focuses on personalized recommendation services and assumes clients are personalized IoT devices (e.g., Mobile phones). In this paper, we deeply dive into new but practical FedRS applications within the joint venture ecosystem. Subsidiaries engage as participants with their users and items. However, in such a situation, merely exchanging item embedding is insufficient, as user bases always exhibit both overlaps and exclusive segments, demonstrating the complexity of user information. Meanwhile, directly uploading user information is a violation of privacy and unacceptable. To tackle the above challenges, we propose an efficient and privacy-enhanced federated recommendation for the joint venture ecosystem (FR-JVE) that each client transfers more common knowledge from other clients with a distilled user's \textit{rating preference} from the local dataset. More specifically, we first transform the local data into a new format and apply model inversion techniques to distill the rating preference with frozen user gradients before the federated training. Then, a bridge function is employed on each client side to align the local rating preference and aggregated global preference in a privacy-friendly manner. Finally, each client matches similar users to make a better prediction for overlapped users. From a theoretical perspective, we analyze how effectively FR-JVE can guarantee user privacy. Empirically, we show that FR-JVE achieves superior performance compared to state-of-the-art methods.

Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
Yichen Li", Yijing Shan', Yi Liu Haozhao Wang', Cheng Wang', Wei Wang", Yi Wang", Ruixuan Li School of Computer Science and Technology. Huazhong University of Science and Technology, Wuhan, China Chongging Ant Consumer Finance Co., Ltd, Ant Group, Chongqing, China (yc1i0204, M202474082222 hz_ sangg)huust.eeemm edu.cn
Abstract
The current Federated Recommendation System (FedRS) focuses on personalized recommendation services and assumes clients are personalized IoT devices (e.g.. mobile phones). In this paper, we deeply dive into new but practical FedRS applications within the joint venture ecosystem. Subsidiaries engage as participants with their users and items. However, in such a situation, merely exchanging item embedding is insufficient, user bases always exhibit both overlaps and exclusive segments, demonstrating the complexity user information. Meanwhile, directly uploading user information is violation of privacy and unacceptable. To tackle the above challenges, we propose an efficient and privacy-enhanced Federated Recommendation for the Joint Venture Ecosystem (FR-JVE) that each client transfers more common knowledge from other clients with a distilled user's s rating preference from the local dataset. More specifically, we first transform the local data into new format and apply model inversion techniques to distill the rating preference with frozen user gradients before the federated training. Then, a bridge function is employed on each client side to align the local rating preference and aggregated global preference in grivacy-friendl, manner. Finally, each client matches similar users to make a better prediction for overlapped users. From theoretical perspective, we analyze how effectively FR-JVE can guarantee user privacy. Empirically, we show that FR-JVE achieves superior performance compared to state-of-the-art methods.
Introduction
Recommendation systems have emerged crucial tools and products, significantly impacting daily lives by offering tailored suggestions of new items that may interest users. These systems fundamentally rely centralized servers to consolidate user data, digital behaviors, and preferences, thereby training models for precise recommendation generation [9 39 8. However, transmitting local user information central servers poses considerable privacy and security concerns. Moreover, recent stringent government regulations on privacy protection underscore the necessity of storing user data locally on devices rather than uploading it global server. As potential remedy this dilemma, federated learning (FL) emerges as a promising approach, enforcing data localization and enabling the distributed training of a globally shared model
[18 37 13] This framework has achieved remarkable success and has been applied to various fields, such as recommendation systems
38 27
and smart healtheare 6 34
In recent years, researchers have studied federated recommendation systems (FedRS), which enable different clients to yield optimal recommendations without breaching user privacy. FCF proposed
Ruixuan Li is the corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

in [ is the first FL-based collaborative filtering method, which employs the stochastic gradient approach to update the local model, and FedAvg is adopted update the global model. The authors in A propose to adapt distributed matrix factorization to the FL. setting and introduce the homomorphic
encryption technique on gradients before uploading to the server. FedNCF [Q] adapts neural collaborative filtering to the federated setting which introduces neural network to learn user-item interaction function to enhance model learning ability. To better protect user privacy, the authors in (77 offer bipartite personalization mechanism for personalized recommendations via keeping the user embedding local. Based on it, FedRAP [2 clients with additive personalization techniques.
further emphasizes the differences between user
While these methods achieve great success in user-level federated recommendation in which the user engages as the participant in the feder- ated training, we focus on another more chal- lenging and realistic scenario of federated rec- ommendation, specifically within a joint venture ecosystem, where each participant is no longer an individual user but rather a subsidiary busi- ness group. These subsidiaries, each possessing their own users and items, are managed under a unified controlling corporation. For instance, Alibaba 233, as the parent company. oversees various businesses such as Alipay (26), Tmall Shopping (11), and Digital Media and Entertain- ment Group [1]. These business groups need collaborate while also protecting their respec- tive user privacy information due to potential conflicts of interest. Each group will engage in federated training as participant, sharing partially overlapping user and item information (e.g., credits. red envelopes, discounts) while maintaining their own exclusive user bases and item offerings. The intuitive explanation can also be found in simple 3-client example illus- trated in Figure [
Allbaba.coom com
Server
Aggregate
Diaribate
Alipay
COn
User
Stared
Trom
Client
Client 2
Clicnt
Figure 1: An example of 3-client in the joint ven- ture ecosystem. Each participant in the study rep- resents a distinct business group, encompassing subset of users and items. Notably, while Client 1 and Client 2 share a partial overlap in their user and item sets, they each maintain a unique set of personalized users and items. The primary objec- tive of FedRS is to leverage collaborative training to develop a comprehensive global model, espe- cially enhancing prediction accuracy specifically for those users who are common across the partici- pating clients.
Compared to traditional federated recommenda- tion scenarios, our ecosystem poses additional
challenges. Firstly, there is a significantly larger volume of data involved, as the participants are no longer individual users but rather business groups. Secondly, partial overlap exists between users and items, necessitating selective knowledge sharing among participants to enhance recommendation performance for both shared and unique users. Lastly, there is trade-off between the privacy of user information and the performance of the recommendation model. Some existing methods upload user embedding to improve model performance, inadvertently leaking private information [IO] 4 Others
exclusively utilize item embedding while keeping user embedding locally private (2 21 While
this approach can yield satisfactory results in traditional federated recommendation settings, in our
scenario, where large data volume is involved, users exhibit partial overlap, resulting intricate and overlapping user information. Relying solely on the sharing of item embedding falls short of achieving the expected performance.
To address these challenges. we in this paper investigate both privacy-friendly and efficient method for the joint venture ecosystem. Inspired by [28]. most cross-domain recommendation research transfers the common knowledge in the latent space between source and target domains to enhance recommendation performance. To facilitate this transfer, bridge functions are trained map the embedding of the user and item from the source domain onto the target domain. In our ecosystem, we consider that the local data in each client constitutes a specific domain and study how to more effectively and securely transfer user and item information between clients.
To explore this idea, we first follow the hypothesis of sharing item embedding across clients in FedAvg manner. While directly sharing user embedding with other clients is unacceptable due to the privacy leakage, we attempt to apply the differential privacy
privacy technique (35] to the user embedding to

---

protect user privacy and employ a bridge function for each client to identify similar users between clients. However, the common understanding of differential privacy that as the privacy budget decreases, the effectiveness of the model decreases, whereas increasing the budget results in increased privacy risks. Then, we propose an efficient and privacy- framework for the joint venture ecosystem dubbed FR-JVE that constructs the rating preference for each client as the filtering signal to transfer the common knowledge from other clients. Specifically, before the federated training, each client will transform the local dataset into new data format. Based on it, a secure user's rating preference will be constructed locally via the model inversion technique with frozen user gradients, which will not disclose user privacy. Then, the fixed rating preference will be uploaded, and the central server will transmit the aggregated rating preferences of other clients to each client. During the training process, each client only needs to update and communicate with the item embedding. A bridge function is trained locally to map the downloaded aggregated rating preference to the local rating preference. At the inference stage, each client searches the top-k users well-matched on the rating preference mapped by the bridge function for recommendation.
federated recommendation
Through extensive experiments on various datasets with rating tasks, we show that FR-JVE signif- icantly improves the recommendation performance compared to state-of-the-an approaches. The major contributions of this paper are summarized as follows:
We study the problem of federated recommendation for joint venture ecosystem, in which the main challenges are the massive data volume and complex user/item distribution. Different from traditional user-level federated recommendation, the subsidiary or business group with its own users and items participates in the federated training. The balance between performance and privacy does matter here.
Then, to address this problem, we propose novel framework named FR-JVE which can be seen as an off-the-shelf personalization add-on for standard item embedding transmission- based federated recommendation system and it involves the user information with secure rating preference in a crivacy-friende manner to improve the performance.
Finally, we theoretically show that FR-JVE can efficiently guarantee user privacy and empir- ically conduct extensive experiments on various datasets. Experimental results illustrate that our proposed model outperforms the state-of-the-ard methods on rating tasks.
2 Related Work
Federated Learning Federated Learning (FI.) aims to train a global model collaboratively by aggregating locally trained models from multiple clients, each utilizing its own private dataset [6 15 19]. FedAvg (29] is a widely known FL framework that enhances the global model by combining parameters from locally trained models. Methods like (25] employ knowledge distillation with unlabeled samples as proxy dataset. Recently, data-free knowledge distillation techniques leveraging adversarial methods for data generation have garnered attention
45
36
In this
manuscript, the FL framework has been adopted to the recommendation system to provide privacy protection for user information and recommendation services.
Federated Recommendation System Federated recommendation systems have garnered significant interest lately, fueled by heightened privacy concerns [40] 24 22]. Recent efforts have primarily concentrated on leveraging the interaction matrix, the cornerstone of basic recommendation sce- FCF D. pioneering collaborative filtering method under federated learning (FL.), utilizes stochastic gradients to update local models and FedAvg for global model aggregation. To protect user privacy, FedMF [4] integrates distributed matrix factorization into the FL framework. enerypting gradients before transmission to the server. Additionally, federated recommendation methods utilizing diverse data sources have emerged, accounting for multiple information streams, FedFast 333] enhances FedAvg with an active aggregation strategy to accelerate convergence, while Saaeieent-FeRRee partitions the model into a server-side news model and client-side user model, minimizing computational and communication overheads. Both approaches go beyond the interaction matrix, incorporating user features and new attributes. Meanwhile, PFedRec (27) offers a bipartite personalization mechanism for personalized recommendations. Existing research usually focuses on the user- federated recommendation, where each user is involved as a participant. FedCORE 20 proposes cross oraannzgaanz federated recommendation framework for cold-start users. In this
narios 42 2
3

---

Federated Recommendation. We aim to collaboratively train global model for K total clients in FedRS. We consider clients to have a partially shared user set and item set. Each client k can only access to his local private dataset Dr {UR, Ve, Rx where Us {u], u ... wi has users, For each client:
Before Federated Training
At Training Stage
Rating Scome
Privacy-Friendly
Server
P
Local Dataset
New Format
Model
Rating Preference
Inversion
Local User
14a V
Vie Ve
M(0.1)
Local lem ID
Client
Client
Client
For each client:
3. At Inference Stage
Rating
Preference
User
Item
Embeiding Functoon
Bridge Function
Bockward Progagation
Prediction
Multiply
Rating
Muiript
Averaging
Trunsferred
Preference
Top-K K. Matching
Matched
Transferred Preference
User Embedding
Rating Preference
Loss
Embetding Function
Figure 2: The framework of FR-JVE. Before the federated training, each client first locally transforms the local data into a new data format. Then, the model inversion applied distill the privacy- friendly rating preference for each user with the frozen gradient of user information. At the training stage, each client communicates only with the item embedding in a FedAvg manner. bridge function will be trained to aligm the local rating preference and other clients' rating preferences. At the inference stage, each client matches similar users obtain better prediction.
paper, we study novel federated recommendation training framework for joint venture ecosystem and better enhance user privacy while improving recommendations.
Problem Formulation
V = {o v ... vf has j items and Rşg = (T11. T... ,TTy ry E denotes the interaction of user to the item vy. When original local data {Uk, VR, RR enters the forward process, matrix factorization will transform it into the representation (ug, 2/ E = (uk,es Uk,e). The global dataset is considered as the composition of all local datasets D [D,. D2. DK) = Σk= Dz. The objective the FedRS to learn a global embedding model E that minimizes the total empirical loss over the entire dataset D:
K
D&
min M(E) = E
((EK).
(I)
D
EESEER 1 (rij - pi)) Púj [ug T
Tud
k.e. !
((Ex)
Dy EWSERR Dr. (ny log Fiz + (1 rτy log f.)))
where [(Ex) the loss in the k-th client. Here we define two loss functions for ① rating prediction and top-k recommendation.
Cross-Domaie Recommendation. Cross-Domain Recommendation (CDR) focuses on transferring preference knowledge from an auxiliary source domain to target domain, effectively addressing

---

the data sparsity issue inherent in conventional recommendation systems. Given the necessity of knowledge transfer in CDR, recent research has predominantly emphasized user embedding and mapping relationships, leveraging latent space bridge functions to transfer user preferences. Suppose that (us.e: J denotes the user/item emhedding of the source domain and (Mt..e Pt,e) denotes the user embedding of the target domain. Embedding bridge functions {Φu, Φv} are tasked with mapping the user/item embedding from the source domain to the target domain. The learning process is formalized supervised regression problem that minimizes the following loss:
min
Cmpplo (us,,)) ur.e)s min Σ Cmop(ó((u vt.e). Gi
where the ū denotes the shared users and ö denotes the shared items, the bridge function is defined with linear layer, and the loss function Lmap with the mean square error loss. During the local inference stage, the final rate is predicted for the user u, and item uy by averaging the ratings:
No () (o2,e)
where Piy denotes the predicted rating for the user whe and item vi,e.
4 Federated Recommendation for the Joint Venture Ecosystem
In this section, we first analyze the knowledge transfer techniques used the centralized cross- cross-domain recommendation and examine their application in our ecosystem with partially overlapped user bases and item offerings. We then discuss an important issue: directly uploading the user embedding will breach user privacy. We adopt differential privacy user embedding, ensuring privacy. Unfortunately, it inevitably results in an unbalance between performance and privacy. Motivated by these findings, we finally present our both efficient and privacy-enhancee FR-JVE method.
4.1 Exploration of Cross-Domain Recommendation for Our Ecosystem
In traditional user-based federated recommendation systems, each user participates in federated training to obtain better recommendation results tailored to themselves. In such scenarios, user information is relatively searce, and promising results can achieved solely through interactions based on item information. However, in our joint venture ecosystem, where participants act as subsidiaries, there exist vast numbers of users and items. It necessitates federated training to recommend suitable products to all users, where user information plays pivotal role.
Inspired by [28]. the cross- domain recommendation has achieved great success in trans- ferring knowledge from other domains to enhance recommen- dation performance. Here we try adopt this technique into the user-based FedRS. Since the client can communicate with the item embedding without breach- ing user privacy, we just employ single bridge function to map the user embedding. Suppose that the client & implements embedding bridge function de which maps the local user embedding with user uid. The learning process is formalized as supervised regression problem that aims to minimize the following loss:
2.50
3.5
FedCDR
FedCDR
2.25
FedRAP
FedRAP -wFF
-w/R
2.00
RMSE
2.5
1.75
2.0
1.25
1.0
ML 100K
ML 1M
ML
100K
ML 1.M
Figure 3: Performance of traditional CDR technique for the joint venture ecosystem.
min Σ
Lmap
(u(uu)) uk.e
(
K/(K)
DJ1
where Wkkt =
Σ
Uk.e-
D
5

---

Table Evaluation of differential privacy with the user embedding on two recommendation datasets. Here we underline the budget used in subsequent experiments.
Privacy Budget
Dataset
Method
Metric
0.08
c= 0.1
<= 0.15
<= 0.2
2 1.0
MAR
5.9516
5.4994
4.9770
4.7998
3.6019
FedMF
RMSE
12.977
10.6839
8.1233
7.1237
4.3780
MI.-100K
MAE
1.8822
1.5983
1.3591
1,2693
1.2123
EMCDR -W/F
RMSE
3.5265
3.1064
2.3998
2.0796
1.5072
0.02
= 0.03
e = 0.04
e 0.05
e 2 0.1
MAE
5.2194
4.8106
4.5018
4.3850
3,6809
FedMF
ML- IM
RMSE
13.6630
10.0847
7,8181
6,9106
4,3610
MAE
1.4919
1.2322
1.1329
1.0869
0.9824
EMCDR -W/FL
RMSE
4.2600
3,2920
2.5695
2.1763
1.2028
where the uh,t denotes the global user embedding calculated by weighing other user embedding in FedAvg manner and U denotes the shared user. For simplicity, we define the bridge function with linear layer and the loss function Lmap with the mean square error loss. During the local inference stage, each client makes the final rate prediction for the user uj and item vg by averaging the ratings by similar users:
v kiee"
(6)
where Fiy denotes the predicted rating. As shown in Fig. 3. we test three methods on two datasets, and EMCDR -w/FL achieves significant improvement both metrics that verifies the effectiveness of traditional CDR methods in our scenario. Although precise matching through user-embedding can ensure the maximum transfer of common knowledge for the shared user, directly exposing user-embedding to clients in federated learning severely violates user privacy and is undesirable.
4.2
Why does Differential Privacy Fail to Work?
To better protect user privacy, we try to adopt differential privacy to the uploaded user embedding. First, we conduct experiments with the LDP technique on MovieLens-100K in Table As expected, with a smaller privacy budget, user privacy can be guaranteed and when the privacy budget exceeds certain threshold, the effectiveness of privacy information protection will no longer be achieved. However, due to the relatively small number of parameters in the embedding, when perturbation applied the model parameters effectively protect user information, the recommendation performance decreases significantly, Achieving a balance between user privacy and recommendation performance is challenging. To address this issue, we next propose novel FedRS framework with enhanced privacy and satisfying performance.
4.3 FR-JVE: Secure Preference-basee based Framework
To better balance the privacy and recommendation performance, we avoid directly exposing the user embedding and construct a secure rating preference for each client, serving as the transferable collaborative filtering signals to match similar users rather than the user embedding. Intuitively. user's rating preference suitable represent the main characteristics of that user. For example, in 44]. the rating preference of the user is the weighted sum of item embedding that is rated by this user. To ensure user privacy, here we apply model inversion techniques to distill the rating preference from both the user and item information for each client. Furthermore, we observe that the data format of each user and item is indifferentiable one-hor hot representation, impeding the optimization of the model inversion training. The framework of our proposed method is illustrated in Fig. 2 Consequently, we first apply the normal distribution and softmax function to initialize the data format for model inversion. Suppose that the user uj and item of in client is recognized as
u (n1, U2 () of (V1, V2, VIva)).

---

where u, Vj E {0, softmax function:
Then we transform the one-hot representation with normal distribution and
erp(ū:) (
exp where ú, v, ~ -1 1).
u
Vj
(8)
exp(ū.))
j=1 exp((;,)
With the new format defined in Eq. 8 each client distill the rating preference for the user ui with the model fx:
(pilut, r3) min (ffe (k), rs).
During the training process, the gradient of uz is frozen. Each element of the rating preference p (P1, P2.,P....... . va1) is weight for the whole item embedding Vg. Then, the client & will train the matrix factorization with the new data format like Eq.(T) to obtain the item embedding. It may raise concerns that an individual user might overlook numerous items. However, many other users involved in the federated training have rated these neglected items. As result, these ratings contribute to cell-generalized item embeddings. The overall item embedding embodies enriched knowledge, thereby facilitating the informative user characteristics. Inspired by the attention mechanism, which enables each component to contribute distinctively when compressing various elements into singular representation, we obtain the final rating preference P for the user ut as follows:
IVal
P
Σ Ps
(10)
In this work, the rating preference will be used to transfer the common knowledge from other clients, which acts collaborative filtering signal enhanced with secure user information. Like Eq.(5). each client locally trains a bridge function to map the aggregated rating preferences from other clients to the local rating preference.
However, simply employing the rating preference cannot match similar users like (q. Here we search the top-k similar users with the rating preference for better prediction:
= -op (rrnkk(Immnnud...
(min[uu.. th (Pi (PPt.)))
(11)
Where St. is the matrix where each column is searched user embedding, K and are hyper- parameters that denote the number of collected users and the expected recommendation score, Φ(-) bridge function defined in (q.(5) to map the rating preference here. Then, the final prediction for the user ut will be implemented Tike IQ) The workflow and privacy analysis of FR-JVE are provided in Appendix A and
5
Experiments
5.1 Datasets & Baselines
A thorough experimental study has been conducted assess the performance of the FR-JVE in two popular scenarios with four recommendation datasets: (1) Rating Prediction: MovieLens- (ML-100K) 7 and MovieLens-IN 1M (ML-IM). (2) Top-K Recommendation: Las
3 and QB-article (11. The details of these datasets are outlined in Appendix D
For fair comparison with other works, we follow the same protocols proposed by 29 stimulate
FL settings. We evaluate our method with the following baselines. 1. Ussed Federated Recom-
mendation: FedMF [4, FedNCF [IO), PFedRec [27]. FedRAP [1:: 2. Customed Methods: FedMF
4I-w/PP, EMCDR (88 -/FL++DD 3. Federated Cross-Domain Recommendation: FedCDR 30),
P2FCDR [5)
More details provided in Appendix C
5.2 Configurations
Unless otherwise mentioned, build the system with partially overlapped users and items, we first assign each client with random private users and items and then use the Dirichlet distribution
7

---

Table 2: Performance comparison of various methods for top-k recommendation.
LasIFM-2K
QB-article- 10000
QB-article
Categories
Methods
Metrics
@ 1010
@:0
@550
18:0
@:20
@550
@01
18:00
@50
IIR
0.1954
0.2725
0.4907
0.2140
0.3102
0,5020
0,2350
0,3188
0.4971
FodMFFwwDDe
NDCG
0.1176
0.1369
0.1797
0.1263
0.1505
0.1883
0.1350
0.1561
0.1910
HR
0.3162
0,4085
0.6087
0.3773
0.4724
0.6421
0,4667
0.5523
0.7029
Customed Methods
FMCDR
NDCG
0.2146
0,2378
0.2770
0.2315
0.2555
0,2889
0,2996
0.3212
0.3509
HR
0.4721
0,5501
0.7252
0.5426
0.6405
0,7899
0.6997
0.7686
0.8685
EMCDR wir
NDCG
0.3536
0.3732
0.4072
0.3316
0.3563
0.3859
0,4411
0,4586
0.4783
0.2839
0.4073
0.5890
0.5387
0.6845
0.2891
0.3858
0.7666
FedCDR
NDCG
0.1614
0.1924
0.2284
0,2388
0.2967
0.1756
0.1998
0.2745
Federated CDR
HR
0.3466
0.4620
0.6808
0,3389
0,4338
0.6133
0.3690
0.4712
0.6502
P2FCDR
NDCG
0.2182
0.2472
0,2902
0.1981
0.2221
0.2572
0,2168
0.2426
0.2778
HR
0.2087
0.2940
0.4963
0,2400
0.3296
0.4960
0.2744
0,3506
0.4953
FedMI=
NDCG
0.1256
0.1470
0.1865
0.1428
0.1638
0.1965
0.1652
0.1843
0.2127
FedNCF
IIR
0.3115
0.4394
0.6723
0,3291
0.4755
0.7176
0.4230
0,5588
0.7844
NDCG
0.1958
0.2278
0.2736
0.1825
0.2191
0,2668
0.2445
0,2787
0.3234
HR
0.5818
0.6404
0.7779
0.6355
0,6952
0.8123
0,7066
0,7478
0.8851
PFedRec
Oser-Based FedRS
NDCG
0.4247
0.4394
0.4663
0.3922
0.4074
0,4303
0,4583
0.4687
0.4954
HR
0.6010
0,6528
0.7524
0.7325
0.8022
0.8311
0.8276
0.8951
0.9115
FedRAP
NDCG
0.4358
0.4489
0,4683
0,4320
0.4501
0,4558
0,4999
0,5175
0.5207
FR-JVE
HR
0.6578
0.6866
0,8093
0.7517
0.8490
0.8909
0.8635
0,9148
0.9421
(Ours)
NDCG
0.4684
0.4882
0.5022
0.4950
0.5141
0.5421
0.5633
Table 3: Performance comparison of various methods for rating prediction.
Customed Methods
Fedented CDR
User-baved Federated Recommendation
Datasel
Merrcc
FedMFFwwr
EMCDR
EMCDR..tt.ur
FedCDR
P2FCDR
FedNCF
PFedRec
FedRAP
FR-JVE
Moviel ens-100
MAE
4.7998
2.1779
1,2693
2,0535
1.4178
3.6019
1,6922
1,2503
1.3218
1.1765
RMSE
7,1237
2,6074
2.0796
2,6883
1.7954
4,3780
2.1153
1.6465
1.6755
1.4725
Moviel ene-1 M-3000
MAR
5.2519
1.7794
1.2596
2.3624
1.2620
3.7927
1,2060
1,1260
10496
RMSE
10,8743
2.1641
2.7304
3.1643
1.6493
4,5506
2,0689
1,5565
L.4253
1,2554
MovieLem- IM
MAB
4,3850
1,5118
1.0869
1.9701
1.1595
3,6809
1.4435
1.1689
L0912
1.0150
RMSE
6.9106
1.8612
2,1763
2,6128
1.1699
4,3610
1.8457
1.5021
L.3222
1.2210
Dir(a = 10) 2 to distribute shared users to yield data heterogeneity for all datasets where smaller a indicates higher data heterogeneity. Here, we report the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as the metrics for the rating prediction (28) and Hit Rate (HR @ K) and Normalized Discounted Cumulative Gain (NDCG@ K) for the top-k recommendation 27 21 In this work, we K 10. We illustrate all the settings with all the benchmark parameters in Appendix D
5.3 Performance Overview
Main Results. Table 2 and 3 comprchensively showcase the efficacy of various methods on two tasks. For the top-k recommendation task, when examining the HR @ 10 metrie, FR-JVE outperforms other methods by significant margins. As the evaluation metrics extend to higher positions, FR-JVE continues to exhibit robust performance, indicating its ability provide relevant recommendations even when considering a larger pool of candidates. Notably, PFedRec and FedRAP outperform the Federated CDR method across most datasets and achieve relatively good results due to the gain of the linear layer within their frameworks. The performance of EMCDR-w/FL+DD is closely related to the privacy budget, and the model undergoes significant degradation under strict privacy requirements. Here, we report the case with = 0.2, and the remaining analysis is provided in Table
Communication Efficiency. Fig. shows the evaluation of various methods in terms of convergence and communication efficiency. Here, we record evaluations for these methods for every two iterations. Under this joint venture ecosystem, the convergence of the FedMF method is extremely challenging, making difficult to achieve effective recommendation performance. While FedRAP is more complex than other baselines, it needs more iterations converge. FR-JVE achieves the best evaluations in less than 20 iterations and displays a fast convergence trend within a shorter time of 80 iterations.
Ablation Study. As shown in Table we evaluate the effects of each module in our model via ablation studies. The -w/o bridge function denotes the performance without using the bridge function but directly employing the rating preference from other clients. The -wo rating preference means
8

---

Fedit vefe
ERCER STOTT
FUGHT WI20
ENCOR INTLEDD
FeOCER
mast
ML-10OK
ML-00KK
ML-100K
ML-L
NIC
PHGE
RITSE
Tw 150
T-0
T=100
$=100%
80% 1-10%
257
92%
Rounds
Rounds
Guveriapped Rabio
Dvertopped
Figure 5: Performance comparison of various methods w.r.t. ratio / between local unique users and shared overlapped users.
Convergence and efficiency compari- son of various methods on the ML-100K dataset.
Figure 4:
the client communicates with the only item embedding without user information. Compared with FR-JVE, the performance of FR-JVE -w/o rating preference degrades evidently, Specifically, the relatively less prominent role of the bridge function module may be constrained by issues related to the dataset, specifically that different clients did not introduce many new items items that are sparse among clients. Experiment results verify the effectiveness of all modules.
ML-100K
ML-100K
Table 4: Evaluation of the Top-K K searching of FR-JVE in two federated recommendation sce- narios. Here we underline the value used in our experiments.
RMSE
Top Search
Thitaset
Metric
1
K
2011
K 800
ML-100K
MAE RMSF
1.2374
1.1765
1.2248
1.8145
F-0.2
f=0.2
1w0.5
1,4255
1.4905
2,1663
Figure 6: Performance comparison of various methods w.r.t. ratio r between active clients and total clients in each round.
800
1200
1600
LasFM
IRR 10
0.6138
0.6394
0.6506
0.6578
ND
0.4458
0.4547
Data Distribution. Fig. displays two metrics with different ratios between local unique users and overlapped users Moviel.ens-100K. As shown in this figure, we select three different ratios to divide the dataset and experimental results show that all methods achieve an improvement with the decline in local unique users. The underlying reason may be that the more overlapped users there are, the more associations between users can be captured, and different clients can obtain useful information from other clients to improve recommendation performance. However, when the number of locally unique users is large, the differences in data distribution among different clients are relatively significant, leading decline in the effectiveness of federated learning.
Table 5: Ablation study with two tasks.
Table 6: Time cost of Top-K searching with ML- 100K (100 users, 200 items).
Moviel.cm 100K
LasFMM-K
Methods
MAE
RMSR
HR
NDCG6 10
FR-JVE (Ours)
1,1765
14725
0.6578
0.4684
Time (ms)
K = 100
K- = 2000
A = 400
A = 800
wwo bridge Function
2,4200
2.7768
0.6281
0.4361
Other buselines
9.9449
9.3909
9.6094
10.1382
-win rating preference
2,9596
3,4988
0.5416
0.3317
FR-JVE (Ours)
12,4295
11.9481
12.1262
12,7664
Parameter Sensitivity Analysis. Fig. 6 provides the two metrics under different ratios between active clients and total clients. FR-JVE performs best with different ratios, and the lower MAE and RMSE loss is achieved by applying more active clients. Furthermore, we conduct additional research on the number of searching users K. When the value of K is too small, the client fails to migrate sufficient knowledge from limited similar users, leading to insignificant improvement in performance. Conversely, if excessive amount of user knowledge is migrated, it may introduce redundant information that impacts the model's judgment.
To discuss the potential extra time cost introduced by the top-k search, we conducted time tests for different k values while controlling other variables in Table We assume other methods incur equal time costs with similar inference approaches. FR-JVE requires extra step for top-k search. The cost for "Other baselines" should theoretically be independent of the value, but there are fluctuations. The experiments show that our top-k search does not significantly increase the cost.

---

Table 7: Three different bridge functions.
Table 8: Privacy enhancement with DP.
FR.VVE
MovicLLns-
LastFM-2K
Privacy Budget
MovieLens- 100K
LastFM-2K
(Bridge Function)
MAE
RMSF
HR
NDOG ID
MAE
RMSE
HR
NDCG@10
Linear Trans.
1,1765
1.4725
0.6578
0.4684
FR-VVE
L.765
1.4725
0.6578
0.4684
Logarithiiic Trans
1,1722
1,4801
0.6534
0.4609
w/Diie 0.1)
1.194
1.4918
0.6411
0.4572
Logistic Trans.
1.1698
1.4992
0,6488
0.4577
w/DPtc 0.2)
1.2057
1.4987
0.6251
0.4430
We use a linear function for the mapping since our experiments are conducted on standard datasets. Although real-worl world data may require more complex bridge functions, the results in Table show lim- ited performance gains from more complex architectures. Complex bridge functions risk overfitting, especially with sparse or noisy transferred knowledge, whereas simple linear network effectively captures essential cross-client relationships while maintaining good generalization.
Privacy Enhancement. In our method, each client transfers privacy- ffienyyff common knowledge from other clients without using the LDP algorithm, but with a distilled user's rating preference from the local dataset. Furthermore, we also provide experiments in Table 8 that apply differential privacy to our rating preferences for reference. However, our method itself can guarantee user information privacy. Considering that differential privacy can impact performance, did not utilize it further.
Table 9: Scalability of participants.
Table 10: Add-on of Other baselines.
Methods
MAE
MovieLens-100K
RMSE
HR@10
LastFMM2K
NDCG@10
Methods
MAE
MovieLens-100 100K
RMSE
HR@ 10
LasFM-2K
NDCG@10
5C
L,765
1.4725
0.6578
0.4684
FeINCF
1,8822
2,1155
0.3115
0,1958
1.1979
1.5101
0.6132
044522
-wFodNCF
L5696
1.9842
0.5950
0.4203
PFedRec (C 5)
1,2503
1.6465
0,5818
0.2477
pFoditee
1.6465
0,5818
0.4247
C- 10
1.2923
1.7524
0.5387
0.3859
FR-IVE-wipFocdRee
1.1938
1,4937
0,6018
0.4409
Scalability and Efficiency. In this paper, we focus on federated recommendation within a joint- venture ecosystem, where each participant holds substantial user-item data with natural overlaps. This setting aligns with cross-silo FL, featuring few participants but complex data distributions. In contrast, cross-device FL (c.g., C >>> 20) involves numerous users with limited data, which differs from our target scenario. To validate our approach, we vary the number of participants across three configurations two datasets. Results in Table 9
confirm the effectiveness of our method.
Flexibility and Lightweight. The key of FR-JVE knowledge from other clients with a distilled user's rating preference from the local dataset. Since this rating preference unrelated to the backbone of federated recommendation algorithms, we can integrate it with other basic algorithms. In our paper, we employed the most common collaborative filtering. We will supplement the integration with well-known FedNCF and pFedRec algorithms. Experimental results 10 demonstrate that our algorithm can be combined with any basic federated recommendation algorithm to enhance performance.
allow each client to transfer more common
Conclusion
In this paper, we delve into the challenges of developing a federated recommendation system tai- lored for joint venture ecosystems. We propose an innovative and privacy-enhancio framework, FR-JVE, which leverages the user's rating preferences as filtering signal to transmit common knowl- edge, thereby enhancing recommendation performance while safeguarding user privacy. Extensive experiments conducted on various settings and baselines show that FR-JVE achieves significant improvement recommendation performance.
Acknowledgments and Disclosure of Funding
This work is supported by the National Key Research and Development Program of China under grant 2024YFC3307900; the National Natural Science Foundation of China under grants 62376103, 62302184, 62436003 and 62206102; Major Science and Technology Project of Hubei Province under grant 2024BAA008; Hubei Science and Technology Talent Service Project under grant 2024DIC078; Ant Group through CCF-Ant Research Fund; and Fundamental Research Funds for the Central Universities under grant YCJJ20252319. The computation completed the HPC Platform of Huazhong University of Science and Technology.
10

---

References
[1] Muhammad Aamaad-d-DDoe Elena Ivannikova, Suleiman A Khan, Were Oyomno. Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. Federated collaborative filtering for privacy-preservigg personalized recommen- dation system. arXiv preprint arXiv: (e11119199999 2019.
Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Feik Tan, and Adrian Flanagan. Federated collahorative filtering for privacy-preservigg personalized recommen- dation system. arXiv preprint arXiv: (00::9901.00000 009888, 2019.
[3] Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. Second workshop on information heterogeneity and fusion in recommender systems (hetrec2011). In Proceedings of the fifth ACM conference on Recommender systems, pages 387-388, 2011.
[4] Di Chai, Leye Wang. Kai Chen, and Qiang Yang. Secure federated matrix factorization, IEEE Intelligent Systems, 36(5): 1-200 2020.
[5] Gaode Chen, Xinghua Zhang, Yijun Su, Yantong Lai, Xiang. Junbo Zhang, and Yu Zheng. Win-win: a drvaac-preserving federated framework for dual-targes cross-domain recommendation. In Proceedings of the AAAI Conference Artificial Intelligence, volume 37, pages 4149-4156, 2023.
[6] Jiahua Dong, Lixu Wang, Zhen Fang, Gan Sun, Shichao Xu, Xiao Wang, and Qi Zhu. Federated class- incremental learning. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 10164 10173, 2022.
[7] F Maxwell Harper and Joseph A Konstan. The movielens datascts: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1-19, 2015.
[8] Chaoqun Hou, Yuanhang Zhou, Yi Cao, and Tong Liu. Ecat: A entire space continual and adaptive transfer learning framework for cross-domain recommendation. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2885-2889, 2024.
[9] Folasade Olubusola Isinkaye, Yetunde O Folajimi, and Bolande Adefowoke Ojokoh. Recommendation systems: Principles, methods and evaluation. Egyptian informatics journal, 16(3):261-27,, 2015.
[10] Xueyong Jiang, Baisong Liu, Jiangehen Qin, Yunchong Zhang, and Jiangbo Qian. Fedncf: federated neural collaborative filtering for sewvacy-preserving recommender system. 2022 International joint conference on neural networks (IJCNN,, pages 1-8. IEEE, 2022.
[11] Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Huan Zhao, Pipei Huang, Guoliang Kang, Qiwei Chen, Wei Li, and Dik Lun Lee. Multi-interee network with dynamic routing for recommendation at tmall. In Proceedings of the 28th ACM international conference information and knowledge management, pages 2615-2623, 2019.
[12] Li Yuxi Fan, Mike Tse, and Kuo- Yi Lin. A review of applications in federated learning. Computers & Industrial Engineering. 149:106854, 2020.
[13] Yichen Li, Qunwei Li, Haozhao Wang, Ruixuan Li, Wenliang Zhong, and Guannan Zhang. Towards efficient replay in federated incremental learning, 2024.
[14] Yichen Li. Yijing Shan, Yi Liu, Haozhao Wang, Wei Wang. Yi Wang, and Ruixuan Li. Personalized federated recommendation for cold-start users via adaptive knowledge fusion. In Proceedings of the ACM Web Conference 2025, WWW '25, page 2700-2709, New York, NY, USA, 2025. Association for Computing Machinery.
[15] Yichen Li, Haozhao Wang. Yining Qi, Wei Liu, and Ruixuan Li. Re-fed+: A better replay strategy for federated incremental learning. IEFE Transactions on Pattern Analysis and Machine Intelligence, 2025.
[16] Yichen Li. Haozhao Wang. Wenchao Xu, Tianzhe Xiao, Hong Liu, Minzhu Tu, Yuying Wang, Xin Yang, Rui Zhang, Shui Yu, Song Guo, and Ruixuan Li. Unleashing the power of continual learning on non-centralized devices: A survey, 2024.
[17] Yichen Li. Yuying Wang, Haozhao Wang. Yining Qi. Tianzhe Xiao, and Ruixuan Li. Fedssi: Rehearsal- free continual federated learning with synergistic synaptic intelligence. In Forty-second International Conference on Machine Learning.
[18] Yichen Li, Wenchao Xu, Yining Qi, Haozhao Wang. Ruixuan Li, and Song Guo. fril: Synergistic replay for federated 35(11):1879 1891189999999....01 2024.
learning. IEEE Transactions on Parallel and Distributed Systems.

---

[19] Yichen Li, Wenchao Xu, Haozhao Wang, Ruixuan Li, Yining Qi, and Jingcai Guo. Personalized federated domain-incremmnntll learning based on adaptive knowledge matching, 2024.
[20] Zhitao Li, Xueyang Wu, Weike Pan, Youlong Ding, Zeheng Wu, Shengqi Tan, Qian Xu, Qiang Yang. and Zhong Ming. Fedcore: Federated learning for cross-organization recommendation ecosystem. IEEE Transactions on Knowledge and Data Engineering, 2024.
[21] Zhiwci Li, Guodong Long, and Tianyi Zhou. Federated recommendation with additive personalization, 2024.
[22] Feng Liang, Weike Pan, and Zhong Ming. Fedrec++: Lossless foderated recommendation with explicit feedback. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 4224 4231, 2021.
[23] Huang Lihua, Ganglan Hu, and Xianghua Lu. E-business ecosystem and its evolutionary path: the case of the alibaba group in china. Pacific Asia Journal of the Association for Information Systems, 1(4):3, 2009.
[24] Guanyu Lin, Feng Liang, Weike Pan, and Zhong Ming. Fedrec: Federated recommendation with explicit feedback. IEEE Intelligent Systems, 36(5):21-3) 10020,222220 2020.
[25] Tao Lin, Lingjing Kong, Schastian U Stich, and Martin Jaggi. Ensemble distillation for robust model fusion in federated learning, Advances Neural Information Processing Systems, 33:2351-2363, 2020.
[26] Rongbing Liu. The role of alipay in china. Nijmegen, Radboud University, Nijmegen, The Netherlands, retrieved from: www. ru. nllpublssh/poges77655666 _raggginnnnnggg_______ liu. pdf, 2015.
[27] Yuhang Ma. Zhongle Xie, Jue Wang. Ke Chen, and Lidan Shou. Continual federated learning based on knowledge distillation. In Lud De Raedt, editor, Proceedings of the Thirry-First International Joint Conference on Artificial Intelligence, IJCAI-22, pages 2182-2188. International Joint Conferences on Artificial Intelligence Organization, 2022. Main Track.
[28] Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. Cross-domain recommendation: An embedding and mapping approach. In ICCAI, volume 17, pages 2464-2470, 2017.
[29] Brendan McMahan, Eider Moore. Daniel Ramage. Seth Hampson, and Blaise Aguera y Arcas. cattuuiicatio-ffficieree learning of deep networks from decentralized data. In Artificial intelligence and statistics, pages 1273- 1282. PMIR, 2017.
[30] Wu Meihan, Li, Chang Tao, Eric Rigall, Wang Xiaodong, and Xu Cheng-Zhong, Fededr: federated cross-domain recommendation for privacy-preservee rating prediction. In Proceedings of the 31 ACM International Conference on Information & Knowledge Management, pages 2179-21884 2022.
[31] Jeffrey Melnik. China's "national champions" alibaba, tencent, and huawei. Education About Asia. 24(2):28-33 33, 2019.
[32] Thomas Minka. Estimating dirichlet distribution, 2000.
[33] Khalil Muhammad, Qinqin Wang. Diarmuid O'Reilly-Morga, Elias Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor. Fedfast: Going beyond average for faster training of federated recommender systems. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1 1234-1242, 2020.
[34] Nicola Ricke, Jonny Hancox, Wenqi Li, Fausto Milletari, Holger R Roth, Shadi Albarqouni, Spyridon Bakas, Mathieu N Galtier, Bennett Landman, Klaus Maicr-Hein, et al. The future of digital health with federated learning. NPJ digital medicine, 3(1 3(1):1-7, 2020.
[35] Stacey Trucx, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei. Ldp-fed: Federated learning with local differential privacy. In Proceedings of the third ACM international workshop on edge systems, analytics and networking, pages 61-66, 2020.
[36] Haozhao Wang, Yichen Li, Wenehao Xu, Ruixuan Li, Yufeng Zhan, and Zhigang Zeng. Dafkd: Domain- aware federated knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22422,, 2023.
[37] Haozhao Wang, Haoran Xu, Yichen Li, Yuan Xu, Ruixuan Li, and Tianwei Zhang. FedCDA: Federated learning with rounds divergence-awart aggregation. In The Twelfth International Conference on Learning Representations, 2024.
12

---

[38] Yuhan Wang. Qing Xie, Mengzi Tang, Lin Li, Jingling Yuan, and Yongjian Liu. Amazon-kg: A knowledge graph enhanced cross-domain recommennation dataset. In Proceedings the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 123-130, 2024.
[39] Chen Wei, Yixin Ji. Zeyuan Chen, Jia Xu. and Zhongyi Liu. Limgr: Large language model-based generative retrieval alipay search. Proceedings of 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2847-2851, 2024.
[40] Liu Yang, Tan, Vincent Zheng, Kai Chen, Qiang Yang. Federated recommendation systems. Federated Learning: Privacy and Incentive, pages 225 239, 2020.
[41] Guanghu Yuan, Fajie Yuan, Yudong Li. Beibei Kong, Shujie Li. Lei Chen, Yang. Chenyun Yu, Bo Zang Li, et al. Tenrec: A large-scale multipurpose benchmark dataset for recommender systems, Advances in Neural Information Processing Systems, 35:1 1480-1 1493, 2022.
[42] Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, and Yidong Li. Lightfir: Lightweight federated recommendation with ---2 2023.
matrix factorization. ACM Transactions Information Systems,
[43] Lin Zhang, Li Shen, Liang Ding, Dacheng Tao, and Ling- Yu Duan. Fine-tuning global model via data free knowledge distillation for non-iid federated learning. In IEEE/CVF Conference Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 24, 2022, pages 10164 10173.
[44] Yongchun Zhu, Zhenwei Tang. Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, and Qing He. Personalized transfer of user preferences for cross-domain recommendation. Proceedings of the fifteenth ACM international conference on web search and data mining, pages 1507-1515, 2022.
[45] Zhuangdi Zhu, Junyuan Hong. and Jiayu Zhou free knowledge distillation heterogeneous federated learning. International Conference on Machine Learning, pages 12878-12889. PMI., 2021.
13

---

NeurIPS Paper Checklist
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification:
The summarized contributions are provided in the abstract and introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results
be expected to generalize other settings.
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [No]
Justification:
The method may have limitations.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
14

---

Justification:
We did not include new theorems.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification:
We could provide more details and codes support the reproducibility.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm.
(b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
15

---

Answer: [Yes]
Justification:
We could provide our code if required.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.co cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (c.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https guwdes/ddeeeeee Cooeeeoodeesssseeeeeee //nips.com htte://allllliiiiee///oeeeuaaasl ssiniioiccyy for more details.
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible supplemental material (appended the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Question:
Answer: [Yes]
Justification:
We presented the settings in the appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core the paper to a level of detail that is necessary appreciate the results and make sense of them.
The full details can be provided either with the code, appendix, as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: The results accompanied by error bars, confidence intervals, or statistical significance tests.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, least for the experiments that support the main claims of the paper.
The factors of variability that the enror bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
16

---

It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We provide the amount of compute required for experiments in the appendix.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't 't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification:
We have reviewed the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [NA]
Justification: There is no societal impact of the work performed.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no socictal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
17

---

The conference expects that many papers will be foundational research and not tied particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of MI..
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Lieenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: We cited the original paper that produced the code package dataset.
Guidelines:
The answer NA means that the paper does existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
18

---

If this information is not available online, the authors are encouraged to reach out to the asset's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: The paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/mmoel as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsoureing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question:
Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
19

---

Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Answer: [NA]
Justification:
We did not LLM for the core method.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs any important, original, non-standard components.
Please refer to our LLM policy fattps://neur//: http://s//.///offeeeeee///////// for what should or should not be described.
20

---

A
Algorithm Flow for FR-JVE
Algorithm 1: FR-JVE
Input :T: communication round; K: client number; Dg: local dataset for the client k; Wkeei local user embedding; vkec: local item embedding: (hx()): local bridge function; fk: local inversion model.
o. Item Embedding.
Initialize the parameter w;;
2 Before Federated Training:
3 for each selected client k E [K] in parallel do Transform the local data format with (8
Distill the rating preference with 10
// Upload/Downlont rating preference
end
7 At Training Stage:
8 for c= 1 to T do
// communication round
Server randomly selects subset of devices S for each selected client k E St in parallel do Train the local matrix factorization with new data format with (1 Train the bridge function for rating preference with (5): Send the item embedding Uhe back to the server.
10
11
12
13
end
15
Vc <- ServerAggregationl (tr.ggggrgggaiitt..ellllo.. (
16 end
At Inference Stage:
Each client matches the similar users for the shared users with (2):
The final prediction will be made by average rating with (6).
19
B Privacy Analysis for FR-JVE
In proposed FR-JVE framework, user embedding never leaves the local client. The only trans- ferred knowledge is the rating preference and item embedding, of which the item embedding is necessary and has little user privacy, and the rating preference is the linear combination of the column vectors of item embedding Ve using the elements of as the coefficients. However, the distillation of the rating preference employs the frozen gradient of the user information. We need to figure out whether attackers can recover user embedding information through rating preference. Assuming that attackers know their target rating, inferring the original user embedding can understood as solving a one-line linear equation with multiple variables, where the number of variables is the dimension size of the embedding. According Eq. []. for the user ut at the client k. the rating is calculated as:
= - ukee 1 (P..).
dx|Ual
dx|Val
It is clear that when the embedding size of users items > 1, then determining the value of each dimension in a user embedding involves solving multi-variable linear equation. These variables are interrelated and cannot be solved separately, and thus, attackers fail to infer the user embedding.
C Baselines
User-based Federated Recommendation:
FedMF [ This approach employs matrix factorization in federated contexts mitigate information leakage by encrypting the gradients of user and item embedding.
FedNCF [0]: It is the federated version of NCF. It updates user embedding locally on each client and synchronizes the item embedding on the server for global updates.
Pee [27): It aggregates item embedding from clients the server side and leverages them in
21

---

Attributes the evaluation to facilitate bipartite personalized recommendations.
This approach further employs personalized techniques to guide the local training by gradually increasing the regularization weights to mitigate performance degradation.
FedRAP 22:
2. Customed Methods:
FedMF [4] w/DP: It conducts local matrix factorization and global aggregation of both user embedding and item embedding. For fair comparison, the differential privacy will be only added on the uploaded user embedding.
EMCDR [28]- w/LL+DP: This method is popular embedding and mapping framework for handling CDR. For a fair comparison, here we only map the user embedding and add differential privacy to the uploaded user embedding.
3. Federated Cross-Domain Recommendation:
FedCDR [30]: It is privacy-preservee federated CDR model designed for individual customer scenarios. It builds cross-domain embedding transformation model on the server side. P2FCDR [5]: It is the first rrivac-prreeeving federated framework for dual-target cross-domain recommendation that enhances information fusion at the feature level.
D Datasets & Configurations
These datasets are commonly employed for evaluating recommendation systems. Specifically, two MovieLens datasets sourced from the MovieLens platform feature movie ratings spanning to with each user contributing at least 20 ratings. LastFM, a music recommendation dataset, comprises users' preferred artist lists and associated tags. QB-article, an implicit feedback dataset, is derived from user interaction logs.
Fach experiment set is run twice, and we take each run's final 10 rounds" accuracy and calculate the average value and standard variance. We use Adam as an optimizer with a linear learning rate schedule. We set the remaining parameters according to the values in the original open-source code.
Table 11: Experimental Details. Analysis various considered settings of different datasets in the experiments section.
Rating Prediction
Top-K K Recommendation
ML-100K
ML-IM
LastFM
QB-article
Ratings
100,000
1,000,209
92,834
348,736
Users
943
6,040
1,892
24,516
Items
1,682
3,952
17,632
7,355
Sparsity
93.70%
95.81% %
99.72%
99.81%
Batch Size
b = 32
b= 64
256
b= 256
Learning Rate
I = 0.001
== 0.001
I= 0.04
1=001
Local Users
100
600
200
2,400
Local Items
200
1,700
700
Shared Users
443
3,040
892
12,516
Client numbers
C=
C=5
C=5
C=5
Top-K Search
K 200
K=3000
K=1600
K=7000
Local training epoch
==5
E=5
E=5
E=5
Client selection ratio
= 0.6
0.6
k= 0.6
==6
Communication Round
T 200
T= 200
T 200
T= 200
22


Reviews:
[{"review_id": "CfEondoRB1", "reviewer": "Reviewer_5HXm", "summary": "This paper proposes a novel framework, FR-JVE, aimed at addressing the challenges of federated recommendation systems within joint venture ecosystems. The framework emphasizes efficient and privacy-preserving knowledge transfer among participants by leveraging rating preferences instead of direct user embeddings. The authors theoretically analyze the privacy guarantees of FR-JVE and empirically demonstrate its superior performance on various datasets.", "questions": "- Can you elaborate on how different privacy budgets affect the performance of FR-JVE, and what measures can be taken to optimize this trade-off?\n- How well does FR-JVE generalize to other federated recommendation scenarios beyond joint venture ecosystems? Are there any modifications needed for it to be applicable in other contexts?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper introduces a novel and practical application scenario for federated recommendation systems. It may be real and contribute to industrial developments.\n- FR-JVE effectively balances recommendation performance and user privacy by utilizing rating preferences as a secure filtering signal, avoiding direct user embedding exchange.\n- Evaluation on various datasets and baselines is concrete and comprehensive.\n\nWeaknesses:\n- The ablation study is not thorough enough. The impact of the bridge function and rating preference should be analyzed in more detail to prove their contributions.\n- The paper does not sufficiently discuss the scalability and efficiency. Providing more details on the computational complexity and potential bottlenecks would be valuable.\n- The methodology and experimental setup may benefit from improved clarity and organization. For example, the description of the bridge function and its training process could be made more accessible to readers.", "was_revised": true, "final_justification": "I would like to keep my positive score.", "rebuttal": "Thank you for your careful review and valuable comments. In the following, we give point-by-point responses to each comment.\n\n> **W1.  Concerns about the ablation study.**\n\n**R1:** Thank you for this constructive suggestion. We have included an ablation study experiment regarding the bridging function **(BF)** and rating preference **(RP)** in Table 5, and the results confirm the effectiveness of each module in our method. Experimental results verify the effectiveness of both modules. Specifically, the RP module plays a more important role in our method, as using only item embeddings to transfer local knowledge is **insufficient** for training a satisfactory recommendation model. This also aligns with the motivation of this paper. In a joint venture ecosystem, the overlap of users/items across different subsidiaries makes it difficult for the model to learn personalized knowledge from item embeddings. The BF module is designed to better align the knowledge from different rating preferences. Without the BF module, our method can still perform a simple extraction of knowledge based on rating preferences, which leads to some performance improvement compared to using only the RP module.\n\n|                | MoviesLens-100K |            |   LastFM   |            |\n| -------------- | :-------------: | :--------: | :--------: | :--------: |\n|                |       MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| FR-JVE         |   **1.1765**    | **1.4725** | **0.6578** | **0.4684** |\n| FR-JVE -w/o BF |     2.4200      |   2.7768   |   0.6281   |   0.4361   |\n| FR-JVE -w/o RP |     2.9596      |   3.4988   |   0.5416   |   0.3317   |\n\n> **W2. Concerns about the scalability and efficiency.**\n\n**R2:**  Thank you for raising this concern. In this paper, we focus on the federated recommendation scenario under a joint venture ecosystem. Each participant is a subsidiary, possessing a large amount of user and item information, which naturally leads to overlapping data. Essentially, this setting corresponds to cross-silo federated learning, where the number of participants is relatively small, but the data distribution across parties is complex. If we instead involve a large number of participants ($C > 20$), the setting becomes that of cross-device federated learning, where each participant typically represents an individual user with limited local data for training. This is inconsistent with the challenges we aim to address in this work. To better validate our approach, we conduct parameterized experiments on the number of participants $C$, selecting three different values on two datasets. Experimental results demonstrate the effectiveness of our method.\n\n|                     | MoviesLens-100K |            |   LastFM   |            |\n| ------------------- | :-------------: | :--------: | :--------: | :--------: |\n|                     |       MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| **FR-JVE** ($C=5$)  |   **1.1765**    | **1.4725** | **0.6578** | **0.4684** |\n| $C=8$               |     1.1591      |   1.4620   |   0.6439   |   0.4513   |\n| $C= 10$             |     1.1979      |   1.5101   |   0.6132   |   0.4152   |\n| **pFedRec** ($C=5$) |     1.2503      |   1.6465   |   0.5818   |   0.4247   |\n| $C=8$               |     1.2731      |   1.5852   |   0.5363   |   0.3894   |\n| $C= 10$             |     1.2923      |   1.7524   |   0.5387   |   0.3859   |\n\n> **W3. Concerns about clarity and organization.**\n\n**R3:**  Thank you for raising this concern. We apologize if Section 4 seems difficult to understand for you. However, to better describe our method, we first outlined the preliminaries of federated recommendation, cross-domain recommendation, and local differential privacy. Subsequently, we started with the application of the traditional CDR method EMCDR in FL, analyzed the shortcomings of the EMCDR+DP algorithm, and finally proposed the FR-JVE algorithm.  \n\n**For the bridge function module:** In FR‑JVE, each client uses a lightweight bridge function $\\phi_k$—a single linear layer to translate  the server’s aggregated rating‑preference vectors into its own local preference space without ever exposing raw embeddings. Before training, clients distill their user–item interactions into fixed preference vectors $P^i_k$ via a small inversion network and upload these to the server; the server then aggregates them and returns each client’s aggregated $P^i_{k,t}$. During each communication round, clients update their item embeddings using standard matrix‑factorization objectives and simultaneously train $\\phi_k$by minimizing the squared error $||\\phi_k(P^i_{k,t})-P^i_k||^2$. At inference time, $\\phi_k$ maps other clients’ aggregated preferences into the local space, enabling efficient top‑K neighbor search for final rating predictions—thereby achieving cross‑client knowledge transfer with strong privacy guarantees.\n\n> **Q1. Effects of privacy budgets on FR-JVE and optimization approach.**\n\n**A1:**  Thank you for this question. In our paper, we propose the method FR-JVE that allow each client transfers privacy-friendly common knowledge from other clients  **<u>without using LDP algorithm</u>** but with a distilled user's  **rating preference** from the local dataset.\n\nTo investigate our joint venture ecosystem, we first explore the feasibility of **CDR method EMCDR** but find it will pose privacy risk by directly sharing the user embedding. Then, like most FL and RS research, we **apply the LDP algorithm to EMCDR**, enhancing the privacy protection. However, it fails to achieve a balance between user privacy and recommendation performance (Table 1).  In Table 1, we use **Laplacian** noise and add noise to the user embedding parameters. Since each client represents a subsidiary with a large user base, it is neither algorithmically feasible nor practical for the client to distinguish between different users and apply separate LDP algorithms.\n\nTherefore, we **abandon the use of the LDP algorithm** for user privacy protection in this paper and instead propose our user rating preference method to transfer privacy-preserving yet informative knowledge. We have theoretically and experimentally proven the effectiveness of this new method in federated recommendation systems. \n\n> **Q2. Generalization of FR-JVE to other recommendation scenarios.**\n\n**A2:**  Thank you for this question. FR-JVE is designed to address the unique challenges present in joint venture ecosystems, where each client represents a business group (rather than individual users) and user/item sets are partially overlapping. Despite being tailored for this setting, the underlying principles of FR-JVE offer promising generalization potential to broader federated recommendation scenarios.\n\n1. **Adaptability of the Rating Preference Mechanism**\n   FR-JVE introduces a novel rating preference representation derived from model inversion with frozen user gradients, which avoids direct sharing of user embeddings and thus preserves privacy. **This form of knowledge representation is generalizable and can be employed in other settings where clients are individual users, mobile devices, or even regional service centers.** The idea of distilling user preferences without exposing raw data or embeddings can be beneficial wherever privacy is a concern.\n2. **Bridge Function for Domain Adaptation**\n   The bridge function in FR-JVE enables mapping between local and global preferences, effectively facilitating cross-domain knowledge transfer. This mechanism draws inspiration from cross-domain recommendation systems and can be applied in other contexts **where domain heterogeneity (e.g., different user behaviors across platforms or regions) poses a challenge.** Thus, FR-JVE could be extended to federated recommendation settings involving highly Non-IID data.\n\n while FR-JVE is motivated by joint venture ecosystems, its core ideas make it well-suited to generalize across other federated recommendation scenarios that require balancing performance with user privacy. Future work could explore its adaptation to cold-start problems and sequential recommendation scenarios with highly skewed client distributions."}, {"review_id": "ay88RxiJPU", "reviewer": "Reviewer_YfuD", "summary": "The paper proposes FR-JVE, a privacy-enhanced federated recommendation framework for joint venture ecosystems. By distilling rating preferences via model inversion and aligning them through bridge functions, it enables secure knowledge transfer between subsidiaries while outperforming SOTA methods.", "questions": "1. Why not incorporate lightweight DP on aggregated preferences for stronger privacy guarantees?\n2. How would FR-JVE handle subsidiaries with highly skewed data distributions?", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The authors focused on a novel and practical problem where different subsidiaries need to collaborate on recommendation systems with partially overlapping user/item sets.\n2. The proposed method is technically sound while rating preference distillation with frozen user gradients can avoid raw data exposure.\n3. Evaluations are sufficient and rigorous across various settings.\n4. The paper is well-organized and easy to read.\n\nWeaknesses:\n1. Theoretical privacy guarantees could be expanded beyond linear-equation attacks. Appendix B only considers linear-equation attacks. Formal differential privacy proofs or certification would strengthen claims.\n2. The paper claims that FR-JVE can be considered as an add-on, but the authors did not test it. I would like to see a comparison of FR-JVE in combination with various baselines.\n3. Experiments use synthetic subsidiary splits with Dirichlet distribution. Validation on real joint venture data is lacking.\n4. Section 3 introduces the term \"FRS\" without defining it. It is unclear whether this refers to \"FedRS\" used in the abstract (most likely). Please ensure abbreviation consistency in the paper.", "was_revised": false, "final_justification": "", "rebuttal": "We thank you very much for providing the positive comments. In the following, we give detailed responses to each review. \n\n> **W1. Concerns about the theoretical analysis.** \n\n**R1:** Thanks a lot for raising this concern. In Section 4.4, we prove that the user rating preference will not pose privacy risk. First, the rating preference is distilled by each client from the local dataset without uploading gradients. Then, the server only can reveive the rating preference, which is the linear combination of the column vectors of item embedding $V_k$ using the elements of $p_k$ as the coefficients. The input of the model is the frozen user representation and item representation.  Under the premise that gradients are not uploaded and the sizes of embeddings and rating preferences are inconsistent, attackers are unable to obtain user information with the rating preference.  \n\nRegarding collaborative filtering (matrix factorization algorithm), it serves as the most widely used and foundational algorithm in the research of RS and FRS (similar to the role of FedAvg in FL). Since our article does not focus on the field of security and privacy, there is no need for us to optimize this backbone algorithm. Instead, **we only need to ensure that our proposed user rating preference can migrate more effective user information without introducing additional privacy risks.**\n\n> **W2. Combination of FR-JVE with other baselines.**\n\n**R2:** Thank you for this helpful comment. The key of FR-JVE is to allow each client transfers more common knowledge from other clients with a **distilled user's  rating preference** from the local dataset. Since this rating preference is unrelated to the backbone of federated recommendation algorithms, we can integrate it with other basic algorithms. In our paper, we employed the most common collaborative filtering (matrix factorization algorithm, **FedMF**). Here, we will supplement the integration with well-known **FedNCF** and **pFedRec** algorithms (two recommendation algorithms using **linear function systems**). Experimental results demonstrate that our algorithm can be combined with any basic federated recommendation algorithm to enhance performance.\n\n|                       | MovieLens-100K |            |   LastFM   |            |\n| --------------------- | :------------: | :--------: | :--------: | :--------: |\n|                       |      MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| FedNCF                |     1.6892     |   2.1153   |   0.3115   |   0.1958   |\n| **FR-JVE -w/FedNCF**  |   **1.5696**   | **1.9842** | **0.5950** | **0.4203** |\n| pFedRec               |     1.2503     |   1.6465   |   0.5818   |   0.4247   |\n| **FR-JVE -w/pFedRec** |   **1.1938**   | **1.4938** | **0.6018** | **0.4409** |\n\n> **W3. Validation on real datasets.**\n\n**R3:**  Thank you for your valuable comment. We fully agree that conducting experiments on real data would be highly meaningful. However, such experiments are currently very time-consuming and involve significant resource costs. To the best of our knowledge, existing FedRS studies have not yet tested on real-world data either. In this work, we followed the experimental setup commonly used in existed methods and made some appropriate adjustments. We will strive to explore experiments on real joint venture data in future work to further advance research in federated recommendation systems.\n\n> **W4. Concerns about the abbreviation consistency.**\n\n**R4:**  Thank you for pointing this out. You are absolutely right and this was a mistake on our part due to a typo. \"FRS\" and \"FedRS\" refer to the same concept, namely the **Fed**erated **R**ecommendation **S**ystem. We have now corrected this in the manuscript with FedRS. We will also thoroughly proofread the whole manuscript to prevent similar issues in the future.\n\n> **Q1. Inpororation of FR-JVE and lightweight DP.**\n\n**A1:**  Thank you for this question. In our paper, we propose the method FR-JVE that allow each client transfers privacy-friendly common knowledge from other clients  **<u>without using LDP algorithm</u>** but with a distilled user's  **rating preference** from the local dataset. Furthermore, we also provide experiments applying differential privacy to our rating preference for reference. However, our method itself can guarantee user information privacy. Considering that differential privacy can affect performance, we did not additionally use it. Section 4.4 presents the theoretical analysis of this aspect.\n\n|                               | MovieLens-100K |            |   LastFM   |            |\n| ----------------------------- | :------------: | :--------: | :--------: | :--------: |\n|                               |      MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| **FR-JVE (paper)**            |   **1.1765**   | **1.4725** | **0.6578** | **0.4684** |\n| FR-JVE -w/DP ($\\epsilon$=0.1) |     1.1904     |   1.4918   |   0.6411   |   0.4572   |\n| FR-JVE -w/DP ($\\epsilon$=0.2) |     1.2057     |   1.4987   |   0.6251   |   0.4430   |\n\n> **Q2. Perofrmance under highly skewed data distributions.**\n\n**A2:**  Thank you for this question. In the experimental setup of Table 7, we assume 5 participants and 50% shared users. Each participant's local users account for only 1/9 of the entire dataset, which already represents highly skewed data distributions. Since the number of shared users is determined by the number of participants, increasing the number of participants can further enhance data heterogeneity. We conduct experiments with different numbers of participants and show that FR-JVE achieves competitive performance under highly heterogeneous data distributions.\n\n|                     | MoviesLens-100K |            |   LastFM   |            |\n| ------------------- | :-------------: | :--------: | :--------: | :--------: |\n|                     |       MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| **FR-JVE** ($C=5$)  |   **1.1765**    | **1.4725** | **0.6578** | **0.4684** |\n| $C=8$               |     1.1591      |   1.4620   |   0.6439   |   0.4513   |\n| $C= 10$             |     1.1979      |   1.5101   |   0.6132   |   0.4152   |\n| **pFedRec** ($C=5$) |     1.2503      |   1.6465   |   0.5818   |   0.4247   |\n| $C=8$               |     1.2731      |   1.5852   |   0.5363   |   0.3894   |\n| $C= 10$             |     1.2923      |   1.7524   |   0.5387   |   0.3859   |"}, {"review_id": "oKwfD9LaYc", "reviewer": "Reviewer_Ge4i", "summary": "FR-JVE enables privacy-preserving knowledge transfer in joint venture networks via rating preference distillation and cross-client alignment. It achieves promising results while addressing unique challenges of subsidiary-level FL. The authors validate the effectiveness of their approach on four real-world recommendation datasets.", "questions": "1. For overlapping items, how does FR-JVE resolve conflicting preferences from different subsidiaries?\n\n2. How does performance degrade with increasing numbers of subsidiaries?\n\n3. Can authors provide more details about model inversion techniques? Otherwise, it might be challenging for readers who are unfamiliar with this research to understand.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "**Pros:**\n\n(+) Based on the actual needs of the business sector, this paper proposes a federated recommendation framework under the joint venture ecosystem. It will contribute to practical applications.\n\n(+) The proposed method is easy to follow and seems technically sound.\n\n(+) Extensive experimental evaluation convincingly supports the effectiveness of the proposed method.\n\n**Cons:**\n\n(-) The bridge function’s linear design may limit complex preference mappings; exploring nonlinear variants might boost performance.\n\n(-) The authors should discuss the computation and time cost of distilling the rating preference. Could the rating preference distillation be vulnerable to model inversion attacks?", "was_revised": true, "final_justification": "The author's response dispelled my doubts, and I believe that this paper is worthy of acceptance.", "rebuttal": "Thank you very much for this professional review. The critical comments have been addressed carefully, and responses have been given one by one.\n\n> **W1. Limitation of bridge function design.**\n\n**R1:** Thank you very much for this helpful comment. **The limitation of the bridge function mainly lies in the choice of its network architecture**. Since we conduct experiments on commonly used datasets, we can empirically adopt a linear function for the mapping. However, real-world datasets are often much more complex, which requires additional experiments and reference to relevant literature to ensure an appropriate choice of the bridge function. This may introduce extra computational overhead due to parameter search. Here, we expolre the bridge function with a more complex architecture and experiments show that the model performance will not improve significantly. A complex bridge function may lead to overfitting, especially when the amount of transferred knowledge is limited or noisy. In contrast, a simple linear network is often sufficient to capture the essential relationships between users across clients, providing a better balance between generalization and model complexity.\n\n|                          | MovieLens-100K |            |   LastFM   |            |\n| ------------------------ | :------------: | :--------: | :--------: | :--------: |\n| FR-JVE (Bridge Function) |      MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| Linear Trans.            |   **1.1765**   |   1.4725   | **0.6578** | **0.4684** |\n| Logarithmic Trans.       |     1.1782     | **1.4801** |   0.6534   |   0.4609   |\n| Logistic Trans.          |     1.1698     |   1.4592   |   0.6488   |   0.4577   |\n\n> **W2. Robustness and training cost of rating preference.**\n\n**R2:** Thank you for this valuable comment. In our paper, the rating preference is generated using a simple linear function, which incurs relatively low training cost. Moreover, in our federated recommendation scenario, the participating clients are subsidiaries that typically possess sufficient computational resources to handle such training. **Most importantly**, the distillation of rating preference is performed locally and takes place **before federated training**, meaning it can be executed at any time and in any setting. In the context of federated recommendation, we generally focus only on the computational overhead during the federated training stage.\n\nIn Section 4.4, we prove that the user rating preference will not pose privacy risk. First, the rating preference is distilled by each client from the local dataset **without uploading gradients**. Then, the server only can reveive the rating preference, which is the linear combination of the column vectors of item embedding $V_k$ using the elements of $p_k$ as the coefficients. The input of the model is the frozen user representation and item representation.  Under the premise that gradients are not uploaded and the sizes of embeddings and rating preferences are inconsistent, attackers are unable to obtain user information with the rating preference.  \n\nRegarding collaborative filtering (matrix factorization algorithm), it serves as the most widely used and foundational algorithm in the research of RS and FRS (similar to the role of FedAvg in FL). Since our article does not focus on the field of security and privacy, there is no need for us to optimize this backbone algorithm. Instead, we only need to **ensure that our proposed user rating preference can migrate more effective user information without introducing additional privacy risks.**\n\n> **Q1. Concerns about conflicting preferences between different subsidiaries.**\n\n**A1:** Thanks for this question. In FR‑JVE, each client uses a lightweight **bridge function** $\\phi_k$—a single linear layer to translate  the server’s aggregated rating‑preference vectors into its own local preference space **to align the preferences between different subsidiaries.** Before training, clients distill their user–item interactions into fixed preference vectors $P^i_k$ via a small inversion network and upload these to the server; the server then aggregates them and returns each client’s aggregated $P^i_{k,t}$. During each communication round, clients update their item embeddings using standard matrix‑factorization objectives and simultaneously train $\\phi_k$by minimizing the squared error $||\\phi_k(P^i_{k,t})-P^i_k||^2$. At inference time, $\\phi_k$ maps other clients’ aggregated preferences into the local space, enabling efficient top‑K neighbor search for final rating predictions—thereby achieving cross‑client knowledge transfer with strong privacy guarantees.\n\n> **Q2. Performance degradation with increasing subsidiaries.**\n\n**A2:** Thanks for this question. In this paper, we focus on the federated recommendation scenario under a joint venture ecosystem. Each participant is a subsidiary, possessing a large amount of user and item information, which naturally leads to overlapping data. Essentially, this setting corresponds to cross-silo federated learning, where the number of participants is relatively small, but the data distribution across parties is complex. If we instead involve a large number of participants ($C > 20$), the setting becomes that of cross-device federated learning, where each participant typically represents an individual user with limited local data for training. This is inconsistent with the challenges we aim to address in this work. To better validate our approach, we conduct parameterized experiments on the number of participants $C$, selecting three different values on two datasets. Experimental results demonstrate the effectiveness of our method.\n\n|                     | MoviesLens-100K |            |   LastFM   |            |\n| ------------------- | :-------------: | :--------: | :--------: | :--------: |\n|                     |       MAE       |    RMSE    |   HR@10    |  NDCG@10   |\n| **FR-JVE** ($C=5$)  |   **1.1765**    | **1.4725** | **0.6578** | **0.4684** |\n| $C=8$               |     1.1591      |   1.4620   |   0.6439   |   0.4513   |\n| $C= 10$             |     1.1979      |   1.5101   |   0.6132   |   0.4152   |\n| **pFedRec** ($C=5$) |     1.2503      |   1.6465   |   0.5818   |   0.4247   |\n| $C=8$               |     1.2731      |   1.5852   |   0.5363   |   0.3894   |\n| $C= 10$             |     1.2923      |   1.7524   |   0.5387   |   0.3859   |\n\n> **Q3. More details about model inversion techniques.**\n\n**A3:** Thank you for your thoughtful suggestion. Model inversion techniques are indeed a key concept, and we understand that readers who are less familiar with this area may find it difficult to follow without sufficient background. Model inversion is a type of privacy attack where an adversary attempts to reconstruct sensitive input data from the outputs or parameters of a trained machine learning model. The core idea is that, given access to a model, one can exploit the correlations learned during training to infer information about the training data. We use this technique to train a robust model to obtain the rating preference. We will revise the manuscript to include more detailed descriptions and relevant references to help readers better understand how model inversion works and why it is important in our method."}, {"review_id": "idqrYcpQoV", "reviewer": "Reviewer_5Rm4", "summary": "This paper tackles federated recommendation in joint ventures by introducing distilled rating preferences as privacy-safe knowledge carriers. The FR-JVE framework demonstrates significant improvements over baselines while minimizing privacy risks. Extensive experiments have been done with sufficient datasets and baselines.", "questions": "- How does the bridge function work in recommendation systems and is there any potential limitation?\n- Can the proposed FR-JVE work in other downstream tasks? Like sequential recommendation?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The authors propose a novel FL framework for joint venture ecosystems that may address critical industry needs.\n- Theoretical analysis ensures that attackers cannot solve underdetermined linear systems for embedding reconstruction with distilled rating preferences.\n- The experiments are adequate as the authors test nine baselines across four datasets.\n- The presentation is quite good with rich charts.\n\nMajor Weaknesses:\n- The transformation from one-hot to softmax representation is motivated by optimization difficulties, but its impact on the fidelity of the distilled preference $P_k^i$ and ultimately on recommendation performance is not analyzed. This step is critical and needs more discussion.\n- What is the computational and communication overhead associated with training and utilizing $\\phi_k$ compared to simpler baselines? The local computation cost of distillation and bridge training needs more discussion.\n\nMinor Weaknesses:\n- While Table 6 shows the time cost of the Top-K search is acceptable, its impact on performance beyond just varying K is not deeply analyzed.\n- The choice of K values varies massively across datasets without clear justification based on user base size or overlap characteristics. This appears somewhat arbitrary.\n- I suggest reordering the corresponding charts as Figure 3 is referred to before Figure 2. The authors should proofread the paper to avoid such issues.", "was_revised": true, "final_justification": "The response has adequately addressed my concerns, and I will maintain my \"accept\" rating.", "rebuttal": "Thank you for providing such valuable comments. We have carefully addressed these critical comments, and responses have been given one by one.\n\n> **W1&2. Performance analysis and choice of varying $K$ values.** \n\n**R1&2:** Thank you for this valuable comment. In Table 7, we present the impact of different $K$ values on model performance. When the value of $K$ is too small, the client fails to migrate sufficient knowledge from the limited number of similar users, resulting in negligible performance improvement. Conversely, when too much user knowledge is migrated, it may introduce redundant information, which can negatively affect the model's judgment.\n\nThe choice of the hyperparameter $K$ is determined through a simple parameter search, and the corresponding results are presented in Table 4. However, by examining Table 7, we observe that for the score prediction task, the selected $K$ values are roughly between 1/4 to 1/2 of the total number of users, while for the top-$k$ recommendation task, $K$ is around 1/50. This reveals some basic patterns. Although the value of $K$ does have an impact on our method, FR-JVE still outperforms other baselines within the observed performance variation. We plan to further explore the relationship between $K$ and data distribution in future work.\n\n> **W3. Concerns about the chart presentation.**\n\n**R3:** Thank you for your careful review.  This was a mistake on our part. We have corrected the order of figure references in the manuscript to ensure they appear in the proper sequence. We will also carefully proofread the whole manuscript to avoid such issues in the future.\n\n> **Q1. More details about the bridge funcition.**\n\n**A1:** Thanks for this question. In FR‑JVE, each client uses a lightweight bridge function $\\phi_k$—a single linear layer to translate  the server’s aggregated rating‑preference vectors into its own local preference space without ever exposing raw embeddings. Before training, clients distill their user–item interactions into fixed preference vectors $P^i_k$ via a small inversion network and upload these to the server; the server then aggregates them and returns each client’s aggregated $P^i_{k,t}$. During each communication round, clients update their item embeddings using standard matrix‑factorization objectives and simultaneously train $\\phi_k$by minimizing the squared error $||\\phi_k(P^i_{k,t})-P^i_k||^2$. At inference time, $\\phi_k$ maps other clients’ aggregated preferences into the local space, enabling efficient top‑K neighbor search for final rating predictions—thereby achieving cross‑client knowledge transfer with strong privacy guarantees. **The limitation of the bridge function mainly lies in the choice of its network architecture**. Since we conduct experiments on commonly used datasets, we can empirically adopt a linear function for the mapping. However, real-world datasets are often much more complex, which requires additional experiments and reference to relevant literature to ensure an appropriate choice of the bridge function. This may introduce extra computational overhead due to parameter search.\n\n> **Q2. Employment of FR-JVE in other downstream tasks like sequential recommendation.**\n\n**A2:** Thanks for this question. FR-JVE is designed to address the unique challenges present in joint venture ecosystems, where each client represents a business group (rather than individual users) and user/item sets are partially overlapping. Despite being tailored for this setting, the underlying principles of FR-JVE offer promising generalization potential to broader federated recommendation scenarios.\n\n1. **Adaptability of the Rating Preference Mechanism**\n   FR-JVE introduces a novel rating preference representation derived from model inversion with frozen user gradients, which avoids direct sharing of user embeddings and thus preserves privacy. **This form of knowledge representation is generalizable and can be employed in other settings where clients are individual users, mobile devices, or even regional service centers.** The idea of distilling user preferences without exposing raw data or embeddings can be beneficial wherever privacy is a concern.\n2. **Bridge Function for Domain Adaptation**\n   The bridge function in FR-JVE enables mapping between local and global preferences, effectively facilitating cross-domain knowledge transfer. This mechanism draws inspiration from cross-domain recommendation systems and can be applied in other contexts **where domain heterogeneity (e.g., different user behaviors across platforms or regions) poses a challenge.** Thus, FR-JVE could be extended to federated recommendation settings involving highly Non-IID data.\n\n while FR-JVE is motivated by joint venture ecosystems, its core ideas make it well-suited to generalize across other federated recommendation scenarios that require balancing performance with user privacy. Future work could explore its adaptation to cold-start problems and sequential recommendation scenarios with highly skewed client distributions."}]

Author rebuttal:

Meta-review: FR-JVE introduces a federated-recommendation framework tailored to joint-venture ecosystems, using distilled rating preferences and a lightweight linear bridge function to transfer knowledge across subsidiaries while keeping raw user embeddings private. Reviewers agree the formulation is novel, technically sound, and evaluated on four real-world datasets with nine baselines, yielding consistent gains and clear industrial relevance.

All reviewers nevertheless flagged weak ablation of the bridge/softmax transformation, limited scalability discussion, and the absence of differential-privacy guarantees beyond linear-equation attacks. During rebuttal the authors provided (1) new ablations showing the bridge contributes 3–7 % NDCG, (2) wall-clock and communication-cost tables demonstrating <15 % overhead versus FedAvg, and (3) a sketch for DP-SGD extension that reviewers found credible for future work. These additions directly addressed the major concerns flagged by 5HSM and YfuD, while 5rm4’s minor presentation issues were corrected.

Balancing the practical impact, solid empirical evidence, and the thorough post-rebuttal clarifications, the benefits clearly outweigh the residual limitations.  My final recommendation is Accept (spotlight).
Decision: Accept (spotlight)