Federated Recommendation with Additive Personalization
Authors: Zhiwei Li
Guodong Long
Tianyi Zhou
Venue: ICLR 2024 poster (2024)
Keywords: Federated Learning
Federated Recommendation System
Primary area: general machine learning (i.e., none of the above)
TL;DR: We present a novel federated recommendation system, named FedRAP, incorporating additive personalization to enhance the performance of recommendation systems in a federated setting.

Abstract: Building recommendation systems via federated learning (FL) is a new emerging challenge for next-generation Internet service. Existing FL models share item embedding across clients while keeping the user embedding private and local on the client side. However, identical item embedding cannot capture users' individual differences in perceiving the same item and may lead to poor personalization. Moreover, dense item embedding in FL results in expensive communication costs and latency. To address these challenges, we propose Federated Recommendation withAdditive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user. FedRAP encourages a sparse global view to save FL's communication cost and enforces the two views to be complementary via two regularizers. We propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for a user, FedRAP adds the two views together to obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recommendation methods and several ablation study baselines. Our code is available at https://github.com/mtics/FedRAP.

Published as a conference paper at ICLR 2024
FEDERATED RECOMMENDATION WITH ADDITIVE PERSONALIZATION
Zhiwei Li"
Guodong Long'
Tianyi Zhou²
Australian AI Institute, Faculty of Engineering and IT, University of Technology Sydney Department of Computer Science, University of Maryland, College Park zhw www.1ieoutlook.com com, guodeng longeuts edu. au, zhou@umiace umd. edu
ABSTRACT
Building recommendation systems via federated learning (FL) new emerg- ing challenge for next-generation Internet service. Existing FL models share item embedding across clients while keeping the user embedding private and local on the client side. However, identical item embedding cannot capture users' individ- ual differences in perceiving the same item and may lead to poor personalization. Moreover, dense item embedding in FL results in expensive communication costs and latency. To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns global view of items via FL and personalized view locally on each user. FedRAP encourages a sparse global view to save FL's communication cost and enforces the two views to be complementary via two regularizers. We propose effective curriculum to learn the local and global views progressively with increasing regularization weights. To produce recommendations for user, FedRAP adds the two views together obtain a personalized item embedding. FedRAP achieves the best performance in FL setting on multiple benchmarks. It outperforms recent federated recom- mendation methods and several ablation study baselines. Our code is available at https://giis www.to/ww//oooooccccccootccceeet
INTRODUCTION
Recommendation systems have emerged as an important tool and product to allocate new items a user likely to be interested in and they deeply change daily lives (Deng et al., 2019). These systems typically rely on servers to aggregate user data, digital activities, and preferences in order to train a model make accurate recommendations (Das et al.) 2017] [Zhang et al. 2023b). However, uploading users' personal data, which often contains sensitive privacy information, to central servers can expose them to significant privacy and security risks (Chai et al. 2020). Furthermore, recent government regulations on privacy protection (Voigt & Von dem Bussche 2017) necessitate that user data need to be stored locally on their devices, rather than being uploaded to a global server. As a potential solution the above problem, federated learning (FI.) (McMahan et al., 2017) enforces data localization and trains a globally shared model a distributed manner, to be specific, by alternating between two fundamental operations, i.e., client-side local model training and server- side aggregation of local models. It achieves great success in some applications such as Google keyboard query suggestions (Hard al. 2018 Chen et al. 2019). However, the heterogeneity across clients, e.g., non-identical data distributions, can significantly slow down the FL convergence, resulting in client drift or poor global model performance on individual clients. A variety of non-
IID FL methods (Zhao et al., 2018 Li et al. 2020: Karimireddy et al.) 2020b] Ma et al. 2022 Yan Long, 2023) is developed to address the challenge by finding a better trade-off between global consensus and local personalization in model training. However, most of them mainly focus similar-type classification tasks, which naturally share many common features, and few approaches designed for recommendation systems, which have severer heterogeneity among users, focus more on personalization, but suffer more from local data deficiency.
Federated Recommendation. In the quest foster knowledge exchange among heterogeneous clients without breaching user privacy, scholars are delving into federated recommendation systems. This has given rise to the burgeoning field of Federated Recommendation Systems (FRSs) (Zhang

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Published as a conference paper at ICLR 2024
at. 2021). FRSs deal with client data originating from a single user, thereby shaping the user's pro- file (Imran et al. 2023] Yuan et al., 2023b). The user profile and rating data stay locally on the client side while the server is allowed to store candidate items' information. To adhere to FL constraints while preserving user privacy, FRSs need to strike a fine balance between communication costs and model precision yield optimal recommendations (Tan et al.] (022b). Several recent approaches
(Lin et al.] 2020 Li et al. 2020, Liang et al.) 2021 Yuan et al. 2023a Zhang et al. 2023ch have emerged confront these hurdles. Most of them share partial model design (Singhal al. 2021]
Pillutla et al. 2022) in which the item embedding is globally shared and trained by the existing FL pipeline while the user funetion/embedding/ is trained and stays local. However, they ignore the het- erogeneity among users in perceiving the same item, i.e., users have different preference to each item and they may focus on different attributes of the item. While PFedRec (Zhang al. 2023a) consid- ers personalized item embedding. it neglects the knowledge sharing and collaborative filtering across users via global item embedding, Moreover, FL of item embedding requires expensive communica- tion of dense matrix between clients and server, especially for users interested in many items.
Main Contributions. To bridge the gap described above in FRSs, we propose Federated Recommendation with Additive Personalization (FedRAP), which balances global knowledge shar- ing and local personalization by applying an additive model to item embedding and reduces com- munication cost/latency with sparsity. FedRAP follows horizontal FL assumption (Alamgir al.) 2022) with distinct user embedding and unique user datasets but shared items. Specifically, the main contributions of FedRAP are follows:
Unlike previous methods, FedRAP integrates two-way personalization: personalized and private user embedding, and item additive personalization via summation of user-specific item embed- ding matrix (4) and globally shared item embedding matrix C updated via server aggregations.
Two regularizers applied: one encouraging the sparsity of C (to reduce the communication cost/overhead) and the other enforcing difference between C and D (to be complementary).
In earlier training, additive personalization may hamper performance due to the time-variance and overlapping between C and (+); mitigate this, regularization weights are incrementally increased by a curriculum transitioning from full to additive personalization.
Therefore, FedRAP is able to utilize locally stored partial ratings to predict user ratings for unrated items by considering both the global view and the user-specific view of the items. In experiments on six real-data benchmarks, FedRAP significantly outperforms SOTA FRS approaches.
RELATED WORK
Personalized Federated Learning. To mitigate the effects of heterogencous and non-IID data,
many works have been proposed (Yang al. 2018 Zhao al. 2018 Ji et al. 2019 Karimireddy
ct al.
Li al. 202 Wu & Wang 2021 Hao et al.) 2021] 2023 Ma et al. 2023). In this paper, we focus on works of personalized federated learning (PFL). Unlike the works that aim to learn a global model,
[020aa Huang et al. 2020 Luo et al. 2021
Wang et al. 2022 Long et al. 2023 Luo al.
PFL seeks to train individualized models for distinct clients (Tan et al. 2022a), often necessitating server-based model aggregation (Arivazhagan et al. 20199; T Dinh et al. 2020 Collins et al. 2021] et al.) 2019; Huang et al. 2020 Dinh et al. 2020) accomplish
Several studies (Ammad-Ud
personalized federated learning by introducing various regularization terms between local and global
models. Mcanwhile, some works (Flanagan et al.] 2020 Li et al. 202 Luo et al.] 2022] Tan
al. 2023) focus on personalized model learning, with the former promoting the closeness of
local models via variance metrics and the latter enhancing this by clustering users into groups and selecting representative users for training, instead of random selection.
Federated Recommendation Systems. FRSs are unique in that they typically have a single user per client (Sun et al. 2022), with privacy-protective recommendations garnering significant research attention. Several FRS models leverage stochastic gradient updates with implicit feedback (Ammad-
Perifanis & Efraimidis) Ud-Din et al. 2019) explicit feedback (Lin et al. 2020 Liang et al. 2021 202iPPrrifanis
2022 Luo et al. 2022). FedMF (Chai et al. (020) introduces the first federated matrix factorization
algorithm based non-negative matrix factorization (Lee & Seung. 2000), albeit with limitations
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in privacy and heterogeneity. Meanwhile, PFedRec (Zhag et al. 2023a) offers bipartite personal- ization mechanism for personalized recommendations, but neglects shared item information, poten- tially degrading performance. Although existing works have demonstrated promising results, they typically only consider user-side personalizations, overlooking diverse emphases on the same items by different users. In contrast, FedRAy AP employs a bipartite personalized algorithm to manage data heterogeneity due to diverse user behaviors, considering both unique and shared item information.
3 FEDERATED RECOMMENDATION WITH ADDITIVE PERSONALIZATION
Taking the fact that users' preferences for items are influenced by the users into consideration, in this research, assume that the ratings the clients determined by the users' preferences, which are affected by both the user information and the item information. Thus, the user information is varied by different users, and the item information each client should be similar, but not the same. Given partial rating data, the goal of FedRAP is to recommend the items users have not visited.
Notations. Let R. [r,, P2, -2..--..7 ,n. )² and m items, where r; E [0, indicates the i-th user's s rating data. Since each client only have one user information as stated above, r; also represents the i-th client's ratings. Moreover, we use D Rm*k to denote the local item embedding on the i-th client, and C Rm to denote the global item embedding. In the recommendation setting, one user may only rate partial items. Thus, introduce Ω ((i.j) the i-th user has rated the j-th item} be the set of rated entries in R.
{0, ) denote that the input rating data with users
3.1 PROBLEM FORMULATION
To retain shared item information across users while enabling item personalization, we propose to use C to learn shared information, and employ (1() to capture item information specific to the i-th user the corresponding i-th client, where
2,2,.... n. FedRAP then uses the summation C and ( to achieve additive personalization of items. Considering R 10,1)"xm, utilize the following formulation for the i-th client to map the predicted rating fig into [0, 1]:
min (.C.D(O)
Σ (ras log Pij + (1 - riy) log (1 ft)))
(1)
(i))
Here, ( represents the predicted rating of the i-th user for the j-th item. Eq. ① minimizes the reconstruction error between the actual rating r; and predicted ratings fry based on rated entries indexed by Ω. To ensure the item information learned by ( and C differs on the i-th client, enforce the differentiation among them with the following equation:
Σ
D -I
max C)
Eqs. 1 and 2 describe the personalization model for each client, and aim to maximize the differ- ence between D($) (6) and C. We note that in the early stages of learning, the item information learned (3))
by (D - and C may be inadequate for recommendation, and additive personalization may thus reduce performance. Thus, considering Eqs. (I). (2), and the aforementioned condition, we propose the following optimization problem L in FedRAP:
min D.C
Σ (JJER
(ris log fig + (1 - r(j) log (1 f()) - -A(a,))) D - +
[CI|,,
(3)
where a is the number of training iterations. We employ the functions A(a,e1) and ((a.,z) to control the regularization strengths, limiting them to values between 0 and e1 or 12, respectively. Here, and 22 are constants that determine the maximum values of the hyperparameters. By incrementally boosting the regularization weights, FedRAP gradually transitions from full personalization to addi- tive personalization concerning the item information. As there may be some redundant information C [[|C]] i) to induce C sparsity, which eliminates the unnecessary
in C, we employ the L, information from C and helps reduce the communication cost between the server and the client. The
whole framework of FedRAI AP is depicted in Fig. T
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Server Aggregation
Upload
Download
Enforce Sparsily
Enforce Difference
Compute Loss
Legend
D
ratings
Additive Personalization
Reconstraction Error
Predicted user ratings
4
User arbolding
Client
1 Local iimm embedding
C
Global embedding
Figure I: The framework FedRAP. For each user-i, FedRAP utilizes its ratings r, as labels to locally train a user embedding u, and local item embedding ( By adding D( to the global item embedding C from the server, i.c., additive personalization, FedRAP creates personalized item embedding based on both shared knowledge and personal perspective and thus produces better recommendations. Since clients and server only communicate C, FedRAP enforce its sparsity to reduce the communication cost and encourage its difference to ( so they are complementary.
3.2 ALGORITHM
Algorithm Design. To address the objective described in Eq. (3), we employ an alternative opti- mization algorithm train model. As shown in Alg. [ the overall workflow of the algorithm is summarized into several steps as follows. We start by initializing C at the server and and ( Each client is assumed to represent a single user. For every round, the server randomly selects subset of clients, represented by S., participate in the training. Clients initially receive the sparse global item embedding C from the server. They then invoke the function ClientUpdate update their parameters accordingly with the learning rate y)- Clients upload the updated (C()) y to the server for aggregation. The server then employs the aggregated C in the succeeding training round. Upon completion of the training process, FedRAP outputs the predicted ratings to guide recommendations. AP upholds user privacy by keep- ing user-specific latent embeddings clients and merely uploading the updated sparse global item embedding to the server. This approach notably reduces both privacy risk and communication costs. Furthermore, FedRAP accommodates user behavioral heterogeneity by locally learning user-specific personalized models at clients. As such, even though user data varies due to individual preferences, FedRAP ensures that data within user adheres to the i.i.d assumption.
at their respective clients (i = ......., n).
Cost Analysis. Regarding the time cost, following Algorithm I the computational complexity on
the i-th client for updating u, ( and C requires cost of Okk)). Once the server receives C from n clients, it takes (kmn) for the server to perform aggregations. Consequently, the total computational complexity of Algorithm I at each iteration is O(kmn). In practice, the algorithm typically converges within 100 iterations. As for the space cost, each client needs to store u, E R* D E IR (m))) and C E (mmxk) requiring O( + n)kk + nm) space. The server, on the other hand, needs to store the updated and aggregated global item embedding, requiring O(nm m))k space. Thus, FedRAP demands a total of ((3nm + n + m))k + nm) space.
3.3 DIFFERENTIAL PRIVACY
Despite the fact that FedRAP only conveys the global item embedding C between the server and clients, there still lurks the potential risk of information leakage.
Property (Revealing local gradient from consecutive C) If server obtain consecutive C returned by client i, it can extract the local gradient with respect to C from the latest update of the corresponding client.
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Ca+ respectively. Published as a conference paper at ICLR 2024
Algorithm Federated Recommendation with Additive Personalization (FedRAP)
Input: R. 52, V1, 22, 7). 81, 12
Initialize:C:
Global Procedure:
1: for each client index i 1,,....... parallel do
2: initialize u, D
3: end for
4: for =
.1 do
5: S. + - randomly select n, from n clients
6:
for each client index S. in parallel do
download C from the server; 8: C(+), f f ClientUpdate(u,, C, D(
7:
9: upload C to the server;
10: end for
C+ nh Ei=1 C
Server Aggregation
end for
13: returm: R = 161, ,e,
ClientUpdate:
1: for b = 1, 2, ...... do
2: calculate the partial gradients Vu, L. VcL and D by differentiating Eq.
3: update u, 11, V/ L;
4: update (()) C VVC;
5: update D D - DWL
6: end for
7: A =o((u; D C());;
8: return: C(.) A
Discussion If client i participates in two consecutive training rounds (denoted as and a+ 1), the server obtains ( returned by the client in both rounds, denoted (())
(a) and ((())
According to Alg. 1, it can be deduced that C(a) (a+1) -
(a+1) where 1j is the learning rate. Since there must exist a constant equal to the server can consequently obtain the local gradient of the objective function with respect to for client i during the (a. + 1)-th training round.
= -c
c
Property shows that server, if in possession of consecutive (ti) from specific client, can derive the local gradient relative to C from the most recent client update, which becomes feasible to decipher sensitive user data from the client's local gradient (Chai al. 2020). This further illustrates that a client should not participate training consecutively twice. Despite our insistence on sparse C and the inclusion of learning rate 7) the gradient data collected by the server, the prospect of user data extraction from (C()) (a+1) persists.
To fortify user privacy, we employ the widely used (e. o)-differential privacy protection technique (Minto al. 202T) on C, where e measures the potential information leakage, acting as privacy budget, while ó permits small probability of deviating slightly from the e-differential privacy assur- ance. To secure (e, d)-Differential Privacy, it's imperative for FedRAP to meet certain prerequisites. Initially, following each computation as outlined in Alg. [ AP secures new gradient w.r.t. C on the i-th client, denoted by VC(), which is limited by pre-defined threshold r:
VC
(C min(1,
|C
(C() is utilized update on the client Upon receipt of all updated (C()) Ken. the server aggregates derive a new C.
Post gradient clipping in Eq. 4
Property 2 (Sensitivity upper bound of C) The sensitivity of C is upper bounded by 2nr n,

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Published as a conference paper at ICLR 2024
Discussion 2 Given two global item embeddings C and C', learned from two datasets that differ only in the data of a single user (denoted as u), respectively. we have:
(VC!") ||C C'Il =11 nh
-C
" IVC "IVC ns
2n/T (5)
ns
n
Property posits that the sensitivity of C cannot exceed 2nr To ensure privacy, we introduce noise derived from the Gaussian distribution (000 e² to disrupt the gradient, where e is proportionate to the sensitivity of C. As stated in McMahan et al. 2018)) 18), applying the Gaussian mechanism (2r,,, n,
n,
during training round ensures privacy tuple of 2nit 2). Following Abadi et (2016), FedRAP leverages the composability and tail bound properties of moment accountants to determine for given budget ò, thus achieving (c, -ddfferrrtt privacy.
4 EXPERIMENTS
4.1 DATASETS
thorough experimental study has been conducted to assess the performance of the introduced FedRAP on six popular recommendation datasets: MovieLens-000K (ML-100K), MovicLens-IN (ML-IM), EeauzzonIIntant--i Grocery (TaFeng), and QB-article (uaan al. 2022). The initial four datasets encompass explicit ratings that range between and 5. As our task is to generate recommendation predictions on data with implicit feedback, any rating higher than in these datasets is assigned TaFeng and QB- article are two implicit feedback datasets, derived from user interaction logs. In each dataset, we include only those users who have rated at least 10 items.
(Vidco). LastFM-2K (LastFM) (Cantador et al. 2011). Ta Feng
4.2 BASELINES
The efficacy of FedRAP assessed against several cutting-edge methods in both centralized and federated settings for validation.
NCF (He et al., 2017). merges matrix factorization with multi-layer perceptrons (MLP) to repre- sent user-item interactions, necessitating the accumulation of all data on the server. We set up three layers of MLP according to the original paper.
LightGCN (He et al. 2017). It is collaborative filtering model for recommendations that combines user-item interactions without additional complexity from side information or auxiliary tasks.
FedMF (Chai et al. 2020). This approach employs matrix factorization in federated contexts to mitigate information Ieakage through the encryption of the gradients of user and item embeddings.
FedNCF (Perifanis & Efraimidis 2022). It is the federated version of NCF, It updates user embed- ding locally on each client and synchronizes the item embedding on the server for global updates.
PeedRec (Zhang et al. 2023a). It aggregates item embeddings from clients on the server side and leverages them the evaluation to facilitate bipartite personalized recommendations.
The implementations of NCF, LightGCN, FedMF and PFedRec are publicly available in correspond- ing papers. Although FedNCF does not provide the code, we have implemented a version based on the code of NCF and the paper of FedNCF. Moreover, developed centralized variant of Fe- dRAP, termed CentRAP, to showcase the learning performance upper bound for FedRAP within the realm of personalized FRSs.
4.3 EXPERIMENTAL SETTING
According to the previous works (He al. 2017 Zhang al. 2023a), for each positive sample,
we randomly selected 4 negative samples. We performed hyperparameter tuning for all methods, the parameter ti of our method FedRAP in the range {10' - ... 0}, and the parameter it of
FedRAP the range {10'} 3... Given that the second and third terms in Eq. 3 ggrradually
come into effect during training, pragmatically set the function \(a,y)) tanh( To 01 and

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M(a,2)) tanh( 10) v2,, where is the number of iterations. For PFedRec and FedRAP, the maximum number of server-client communications and client-side training iterations were set to 100 and 10 respectively. For NCF, FedMF and FedNCF, set the number of training iterations to 200. To be fair, all methods used fixed latent embedding dimensions of 32 and a batch size of 2048. We did not employ any pre-training for any of the methods, did we encrypt the gradients of FedMF, Following precedent (He et al. 2017 Zhang et al. 2023a), we used leave-one-out strategy for dataset split evaluation.
Evaluation Metries In the experiments, the prediction performances are evaluated by two widely used metries: Hit Rate (HR @ @) and Normalized Discounted Cunulative Gain (NDCG@K) K). These criteria has been formally defined in He et al. (2015). In this work, we set K = 10, and repeat all experiments by five times. We report the average values and standard deviations of the results.
100
100
NDCG@10
HR@10
50
50
baring ML-100K.
MM
Vidoo
LasiFM CE ertic
TaFang ML100K ML-1
Video
LaATH QB-aricee
NCF
LIghTGNN
FedMF
FedNCF
PFedRec
CentRAP
FedRAP
Figure 2: Evaluation metries (%) on six real-world recommendation datasets. CentRAP is a central- ized version (upper bound) of FedRAP. FedRAP outperforms all the FL methods by large margin.
4.4 MAIN RESULTS AND COMPARISONS
Fig. 2 illustrates the experimental results in percentages of all the comparing methods on the six real-world datasets, exhibiting that FedRAP outperforms the others in most scenarios and achieves best among all federated methods. The performance superiority probably comes from the ability of FedRAP on bipartite personalizations of both user information and item information. Moreover, the possible reason why FedRAP performs better than PFedRec is that FedRAP is able to personal- ize item information while retaining common information of items, thus avoiding information loss. all datasets compared
CentRAP exhibits slightly better performance the performance upper bound of FedRAP on the used datasets. In addition, to investigate the con- vergence of FedRAP, during training on the ML-100K dataset. Because NCF, FedMF and FedNCF are trained of 200 iterations, we record evaluations for these methods every two iterations. Fig. 3 shows that on both HR @ 10 and NDCG@ 10, Fe achieves the best evaluations in less than 10 iterations and shows a convergence trend within 100 iterations. The experimental results demonstrate the superiority of FedRAP. But since FedRAP is more complex than PFedRec, it needs more iterations converge.
AP, demonstrating
compare the evaluations of all methods except CentRAP for each iteration
HR8810
JDCG@810
010
0.
0.0
24
allon
lteration
FcdMF
FesNCS
RFodRoc
FodRAP
fedRAP-cn
PecR/P-ftc
Figure 3: Convergence and efficiency compari- son of four methods on the ML-100K dataset.
Figure 4: Difffrent curriculum for Xaa, v1) in Eq. 3
with 0.1 and to be the iteration.
4.5 ABLATION STUDY
To examine the effects of FedRAP's components, we introduce several variants: FedRAP-C, FedRAP-D, FedRAP-No, FedRAP-L2, FedRAP-fieed. FedRAP-sin, FedRAP-square, and FedRAP- frac, defined as follows: (1) FedRAP-C: Uses a common item embedding C for all users, ignoring
personalization; (2) FedRAP-D: Creates user-specific item embeddings O(), without shared item information; (3) FedRAP-No: Removes item sparsity constraint C, i.e., |[[|| is omitted; (4)
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FedRAP-L2: Uses Frobenius Norm, || instead of ||C|| for constraint; (5) FedRAP-fixed: Fixes )() and P(a,z2) to be two constants U1 and 12, respectively; (6) FedRAP-sin: Applies ((o.0)) = sin a U1 and M(a,e)) sin( [n) * U2: (7) FedRAP-square: Alternates the value of M(a,,,) and M(a,v)) between 0 and U1 22 every 10 iterations, respectively; (8) FedRAP-frace Utilizes A(a,e)) = _i and M(a,vs) - Here, denotes the number of iterations and v is a constant. FedRAP-C and FedRAP-D validate the basic assumption of user preference influencing ratings. TedRAP-No and FedRAP-L2 assess the impact of sparsity in C on recommendations. The last four variants explore different weight curricula on FedRAP's performance. We set V1 and 1/2 to 0.1 for all variants, and Fig. depicts the hyperparameter trends (a.,,) of for the final four variants. The trend of ((,,2)) is the same as the corresponding A(a.v)) of each variant.
a+T
Ablation Study of FedRAP. To verify the effectiveness of FedRAP, we train all variants as well Ae the MI.-100K dataset. In addition, since PFedRec is similar to FedRAP-D.
also compare it together in this case. shown in Fig. 5 we plot the trend of HR @ 10 and NDCG@ 10 for these six methods over 100 iterations. From Fig. 5
that FedRAP-C performs the worst, followed by FedRAP-D and PFedRec. These indicate the importance of not only personalizing the item information that is user-related, but also keeping the parts of the item information that are non ron-lusr-eelated. In addition, both FedRAP-C and FedRAP-D reach convergence faster than FedRAP. Because FedRAP-C trains a global item embedding C for all clients, and FedRAP-D does not consider C, while FedRAP is more complex compared to these two algorithms, which explains the slow convergence speed of FedR AP because the model is more complex. The figure also shows that the performance of FedRAP-D D and PFedRec is similar, but the curve of FedRAP-D is smoother, probably because both have the same assumptions and similar thcories, but we add regularization terms to FedRAP. The results of FedRAP-No. FedRAP-L2 and FedRAP are very close, where the performance of AP is slightly better than the other two as seen in Fig. b)). Thus, we choose to use L1 norm to induce C to become sparse. Also, sparse C helps reduce the communication overhead between the server and clients.
1.
H0,910
LDCG
62
teration
F
FodRAP No
iteration
RedRAP
110
0.00
Iteration
Iteration
1sdRAPD
12
Pheditec
Bolke fox
Figure 5: Ablation study investigating the effec- tiveness of FedRAP on the ML-100K dataset.
Figure 6: Comparison of different curricula for X(a,01) and ((a,02) in FedRAP on the ML- 100K dataset.
Ablation Study on Curriculum. In this section, we investigate the suitability of additive per- sonalization in the initial training phase by analyzing the performance of FedRAP and its four weight variations (FedRAP-fieed, FedRAP-sin, FedRAP-square and FedRAP-frac) on HR @ and NDCG@ @ 10 in the validation set of the ML-100K dataset. Fig. ssoows that FedRAP-fixed with con- stant weights, underperforms FedRAP, which adjusts weights from small to large. This confirms that etrly-stage additive personalization leads performance degradation, item embeddings have not yet captured enough useful information. Furthermore, Ae-Rap- which reduces weights from large to small, performs the worst, while FedRAP performs the worst, reiterating that early-stage additive personalization can decrease performance. Therefore, we choose to use the tanh function.
Ablation Study on Sparsity of C. Since FedRAP only exchanges C between server and clients, retaining user-related information the client side, we further explore the impact of sparsity con- straints C in FedRAP by comparing the data distribution of C and (D)) ()), learned in the final iteration when training both FedRAP and FedRAP-L2 on the ML-100K dataset. We select three local item embeddings for comparison: D (43) D (586), and D (786)_ along with the global item em- bedding C. For visualization, we set latent embedding dimensions to 16 and choose 16 items. Fig. reveals that FedRAP's global item embedding C is sparser than that of FedRAP-L2. We also notice that 4. 19% of FedR AP's C parameters exceed le-1 and 67.36% exceed le-2, while for FedRAP-1.2,
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D
D
D
0
9:1224
101214
0 2 4 101214
0 6112214
5 a.0.0. o
Items
Items
Items
12
Features
Features
Features
Features
(a) FedRAP-L2
D(42)
D
Diess
0 7 0001224
2 10:214
4 10121
0.2 201214
o a. o
Items
Items
Items
Features
Features
Features
Features
(b) FedRAP
Figure 7: Sparse global matrix C and row-dense personalized matrix D for item embedding.
these values are 8.07% and 88.94% respectively. This suggests that FedRAP's sparsity constraint reduces communication overhead. Additionally, both methods learn similar ( (i 46, 586, 785), with notable differences between distinct ( This supports our hypothesis that user preferences influence item ratings, leading to data heterogeneity in federated settings and validating FedRAP's ability learn personalized information for each user.
FedRAP with Differential Privacy. To protect user privacy, we introduce noise from the Gaus- sian distribution the gradient w.r.t on clients and compare the performance FedRAP with and without noise. In this work, we set sian Mechanism following the previous work (McMahan et al. 2018). We use the Opacus libaray YYousetpour et al. (02I) to implement the differential privacy. Table | presents results for these approaches on six datasets, showing reduced FedRAP performance after noise addition. However, Fig 2 indicates, it still outperforms benchmark methods. Moreover, since FedRAP only requires the transmission of C between the server and clients, adding noise solely to is sufficient for en- suring privacy protection. This results in mitigated performance decline due to differential privacy compared to adding noise to all item embeddings.
0.1, and fix the noise variance 2 = for Gaus-
Table I: FedRAP vs. FedRAP-noie (FedRAP with injected noise for differential privacy)
Dinaact
TaFong
MILTOO
ML.IMM
Vidco
LasPM
GB-artide
Metriss
HR
1DCG
HR
NDCG@10
HR
NDCG011
10
NDCGO110 10
10
NDOG 10
HR 10
NDOG0110
FoaRAP
039433
00911
B970
0.77
03334
0.7187
0.6651
03317
0.233)
1010
0.5395
0.2598
FoiR P.IRA
8.536
09365
89364
0.0055
0.9100
0.7035
0.4911
0.3645
0.2266
0.1089
0.5020
0.2475
Degradaion
4.09%
551 L
3.55
8.26 L
1.54
2.18 3
9.38
6.11% J
2.62%
0.91% ]
7.00%
4.73% 1
CONCLUSIONS
In this paper, based the assumption that user's ratings of items are influenced by user preferences, we propose a method named FedRAP to make bipartite personalized federated recommendations. i... the personalization of user information and the additive personalization of item information. We achieves a curriculum from full personalization to additive personalization by gradually increasing the regularization weights to mitigate the performance degradation caused by using the additive per- sonalization at the early stages. In addition, sparsity constraint on the global item embedding to remove useless information for recommendation. which also helps reduce the communication cost. Since the client only uploads the updated global item embedding the server in each iteration, thus FedRAP avoids the leakage of user information. We demonstrate the effectiveness our model through comparative experiments
six widely-used real-world recommendation datasets, and nu- merous ablation studies. Also, due to the simplicity of FedRAP, it would be interesting to explore its applications in other federated scenarios.

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A THEORETICAL ANALYSIS
In this section we give further details on the theoretical results presented Section 3.4. We start by giving the exact conditions needed for Theorem to hold.
A.I FULL DISCUSSION OF THEOREM
To delineate Theorem 1, we first re-define the formula for updating C(1) at the i-th client based on Alg.
c( (a+1) C(a) () (a+1))
(6)
where y is the learning rate, and ( stands for the gradients of Eq. (3) w.r.t. C on the client in the a + 1-th iteration. When the server receipts all the updated {C(2 13 1hh it then perform the
aggregation to get C(a-1). Thus, if the client participate the training in two consecutive rounds,
we can get the difference between c(i
(a+1) and c(O) by the following fomula:
C (4) (a+1) (a)
VC (a+1))
Since there exists a constant equal to the pre-defined learning rate n, an attacker can easily obtain the gradient (C(i) (a+1) of client in the a-th round from the server. According Chai et al. (2020), if the client participates in sufficient number of training rounds consecutively, the (C) obtained by the server would leak information from that client's 's local data. This emphasizes that a client should avoid participating in training consecutively.
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A.2 OPTIMIZATION ANALYSIS
While the optimization of the objective function not jointly convex in all variables, it exhibits convexity with respect each individual variable when the others are held fixed. Consequently, can pursue an alternate optimization strategy for each of the variables within the objective function in Eq. 3 Denoting X1 = (D( + C)2- X2 = + exp(-u? x1) and X3 = =1+ exp( us), we can develop an alternating algorithm to update the parameters U, C, [D
A.2.1
UPDATE U
Since the updating happens on each client, when and Df fixed, the problem w.r.t u, becomes:
min
(ris log 1/(te
<u.. <u.
+ +1 log(l
(u, (1 (8)
(L))eR
Assume that X1 = (6) + C)2 and X2 = 1 + -erppp(-u xi ), we have the partial derivative of L(u:)
following:
Vu,C= C Σ -((el_((x)) (1- r(s))/((33 (1=1/x))) x1 + (ris x() x1). (9)
We then update u, on each client by gradient descent:
u, f u, V u.
(10)
A.2.2
UPDATE C
On each client, when fixing u; and (6), the problem becomes:
min Σ
(rig log 1/(1 +e (D() +))>) (1 - r.y) log(1 1/(1 e (D. +0)2)))
(K))
Σ X(amm)
| CCI Mama))|
(n))
(11)
We first calculate the first two terms in Eq. equation 11
X3 1) (1 riy)
VcL = Σ
(x3 (1 1) u, + Tij (Xg 1)) u,) + 22 (C -
DCC (1).
(12)
(ifER
As for the constraint
||[||,, we need to use the soft shrt-thre
DDnoho 1995) to update C:
SyC v/VcC),
(13)
where 0 is a dre-ne thresholding value, Sg the shrinkage operator, and is defined So(u)
max(u 8,0) max(-e- 8, 0).
A.2.3
UPDATE (
When the others fixed, the problem w.r.t. ( becomes:
Σ
(rig log 1/(1 + (D +0))) (1 roy) - 1/(1 + (D. +C))+++++)))))
min
C
(t))
Σ
D -C
(14)
Each client then obtains the partial derivative of ((D()) following:
(x3 1) Tig)
D
Σ
u, rij (X3 - 1)) u:) + 2 2A (D (D(
C). (15)
(1-
(UJ)R
We can update D (f) on the client i by the following equation:
( D (6) V/ NoDl
(16)
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B MORE EXPERIMENT DETAILS
In this section, we present some additional details and results of our experiments. To be specific, all models were implemented using PyTorch (Paszke al. 2019), and experiments were conducted on machine equipped with a 2.5GHz 14-Core Intel Core 19- 1-109 processor, RTX 3070 Ti Laptop GPU, and 64GB of memory.
The statistic information of the datasets used in the research. #Ratings is the number of the observed ratings; #Users is the number of users; #Items is the number of items; Sparsity is percentage of #Ratings in (#Users x #Items).
Table 2:
Datasets
#Ratings
#Users
#Items
Sparsity
TaFeng
100,000
120
32,266
78.88%
MI.-100k
100,000
943
1,682
93.70%
ML.-MM
1,000,209
6,040
3,706
95.53%
Video
23,181
1,372
7,957
99.79%
LastFM
92,780
1,874
17,612
99. 72%
QB- artiilee
266,356
24,516
7,455
99.81%
B. DATASETS
All the six popular recommendation datasets: MovieLens-100K (ML-100K)', Mov.eLLns--lll IM (ML- 1M)' Amazon-Instaie Video (Video)?, LastFM-2K (LastFM)), Ta Feng Grocery (TaFeng)*, and QB-article" are public. Table presents the statistical information of the six datasets used in this study. All datasets show a high level of sparsity, with the percentage of observed ratings to the total possible ratings (users times items) all above 90%.
B.2 COMPRISON ANALYSIS
Tables and 4 present experimental results comparing various centralized and federated methods across Six real- datasets, with the measurements focused on HR @ 10 and NDCG@ 10, re- spectively. In Table 3 it's evident that FedRAP consistently outperforms other federated methods (FedMF, FedNCF, and PFedRec) terms of HR across all datasets. Remarkably, FedRAP also surpasses the centralized method NCF and approaches the performance of CentRAP, a tralized variant of FedRAP that inherently leverages more data than FedRAP. Table 4 presents a similar trend in terms of NDCG@ 10. Again, FedRAP demonstrates superior performance among federated methods, and closely competes with the centralized methods, even surpassing Ce in some datasets. These results substantiate the effectiveness of FedRAP balancing personalized item recommendation with privacy preservation in federated setting. It also highlights FedRAP's capability to handle heterogeneous data across clients while still achieving competitive performance with centralized methods that have full access to all user data.
Table 3: Experimental results on HR@ 10 shown in percentages on six real-world datasets. Cent and Fed represent centralized and federated methods, respectively, The best results are highlighted boldface.
Dataset
TaFeng
MI-1008
MI.IM
Video
LastEM
QB antile
NCF
98.33 = 1.42
65.94
0.38
62.52 0.70
0.99
20.81 = 0.69
40.94 : 0.16
Cent
LightGCN
99.33 0.51
99.54
0.99
99.43 0.31
0.54
28.56 - 0.97
61.09 0.63
CentRAP
99.87 - 0.97
99.30 + 0.64
99.17 + 0143
48.54
0.332
24.76 + 0.12
54.28 + 0.51
FedMF
97.50 = 1.11
65.05
0.84
15.951..3
39.00:0.16
20.04
0.20
40.54
FedNCF
87.54 1.01
60.89
0.24
61.31
0.32
39.25 101
20.14
40.52 0.57
PFedRec
98.33
1.05
70.03
0.27
68.59 + 0.21
40.87
0.88
21.61 + 0.45
46.00 - 0.22
FedRAP
99.43 1.44
97.09 : 0.56
93.24 1 0.26
46.53
0.48
23.29
1.19
53,98 0.57
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Table 4: Experimental results NDCG@ 10 shown in percentages six real-world datasets. Cent and Fed represent centralized and federated methods, respectively. The best results are highlighted boldface.
Dataset
TaFeng
ML-100K
ML-IM
Video
LastFM-2K
QB-article
NCF
97.53
38.76 1 0.61
33.60 1 0.56
21.94
11.36 0.34
19.61 : 0.37
Cent
LightGCN
98.7 1.47
91.52 = 0.86
90.43
32,64 1.13
1.00
31.85 = 0.96
CentRAP
99.54 1.35
86.65 0.51
86.89 - 0.46
28.8
11.30
= 0.54
FedMF
96.72
38.40
38.77 0.29
25.08:10..5
17.76 0.18
Fed
FodNCF
83.42 1.71
32.68 0.47
34,94
21.72
9,99 0.64
19.31 0.67
PRedRec
98.03
41.38 1 0.33
40.19 0:21
24.04
0.36
10.88
0.10
22.28 0.28
FodRAP
99.11 2.31
87.81
2,00
71.87
0.28
28.17
0.22
10,99 0.51
24.75 0.36
0.8
0.8
0,8
0.6
0.6
0.6
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
for the 43-th user
C' for the 586-th user
(c) C for the 785-th user
1.0
1.0
1.0
0.4
0.4
0.4
0.2
0.2
0.2
0.0
0.0
0.0
(d) D 43
(e) D 586
(l) D 785
1.0
0.8
0.8
0.8
0.6
0.5
0.6
0.4
0.4
0.4
0.0
6.0
0,2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1,0
6.a
0.2
0.4
0.6
0.8
1.0
(g)
C
43
(h)
588
(i)
D
Figure 8: t-SNE visualization of item embeddings learnd by FedRAP.
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Published as a conference paper at ICLR 2024
B.3 ABLATION STUDY ON ITEM PERSONALIZATION
In this section, we visually demonstrate the benefits of FP for personalizing item information. FedRAP not only personalizes user information, but also implements the additive personalization to item information, thus it achieves the bipartite personalization to perform user-specific recommen- dations. In this section, we investigate the roles of the global item embedding C and the local item embeddings (D()) Ji=1 in FedRAP. To visualize them, we select three local item embeddings D (43), D (580)) D (785), and the global item embedding C from the models learned on the Moviel.ens 100K dataset according to Sec. 4.3. Since in this paper, we mainly focus on the implicit feedback recom- mendation, i.e., each item is either rated unrated by the users, map these item embeddings into a 2-D space through (-SNE (Van der Maaten & Hinton 2008), and the normalized results are shown in Fig. [ where purple represents items that have not been rated by the user, and red represents items that have been rated by the user. From Figs. 8 (c), we can see that the items rated and unrated by users are mixed together in C, which depicts that C only keeps information about items shared among users. And Figs. (d)-(f) show that the items learned by ( can by obviously divided into two clusters for the i-th user by FedRAP. This demonstrates that ( can only learn information about items related to the i-th user, such as the user's preferences for items. Morover, we add C and ( show the additive personalization of items for the i-th user, which is shown in Figs. g (i). These three figures again demonstrate the ability of FedRAP to effectively personalize item information. This case supports the additive personalization of item information to help make user-specific recommendations.
Table 5: Comparison of Performance Degrada- Table 6: Comparison of Performance Degrada- tion with and without Differential Privacy tion with and without Differential Privacy HR@10.
NDCG@10 10.
w/ DP
w/o DP
Degradation
DP
w/o DP
Degradation
FedMF
0.6505
0.6257
3.82%
FedMF
0.3840
0.3451
10.13%
FedNCF
0.6089
0.4348
28 60%
FedNCF
0.3268
0.2386
27.00%
PFedRec
0.7003
0.6352
9.30%
PFedRec
0.4138
0.3549
14,23%
FedRAP
0.9709
0.9364
3.55%
FedRAP
0.8781
0.8015
8.72%
B.4 ABLATION STUDY ON DIFFERENTIAL PRIVACY
We further evaluated DP versions of the used federated methods and compared them with the origi- nal methods in Tan Table which shows FedRAP with DP can still preserve the performance advantage of FedRAP and meanwhile achieve (e, DD-P.
B.5 ABLATION STUDY ON FACTOR EFFECTS
To examine factors affecting FedRAP's performance, we designed ML-1MM dataset variations with different sparsity levels by selecting specific numbers of users or items. Table 7 displays FedRAP's perfor- mance on these datasets, and shows that as sparsity increases, performance declines more significantly. Additionally, with sim- ilar sparsity, increasing user count leads to slightly reduced performance. Datasets with a fixed number of items consis- tently underperform compared to those with equal user counts. This table indi- cates that FedRAP's effectiveness is influ- enced by dataset sparsity and the counts users and items.
Table 7: Sparsity of various subsets in ML-1M and FedRAP's fine-grained performance (HR @ 10 and NDCG@ 10).
Subset
Sparsity
HR 10
NDCG@ 10
81.33%
1.0000
0,9446
w/ 500
95.49%
1.0000
0.8227
98.62%
0.9020
0.5686
85.87%
1.0000
0.9064
w/ 1000 users
97.52%
0.9990
0.7118
98.97%
(.8260
0.5207
87.62%
0.9851
0.8931
w/ 1000 items
94.86%
0.9398
0.7759
96.8.5%
0.9186
0.7003
17

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Published as a conference paper at ICLR 2024
C DISCUSSION ON LIMIATION
While FedRAP has shown excellent performance federated recommendation systems through techniques like additive personalization and variable weights, limitation is that the current Fe- dRAP algorithm requires storing the complete item embedding matrix on each user's device. This could demand significant storage space, especially the total number of items large. simple approach to alleviate this limitation is to only store the embeddings of items that each user has inter- acted with. However, this modification may require extra attention the cold-star problem of new items for each user. Our future work will aim to address this limitation.
18


Reviews:
[{"review_id": "muO59qDtTY", "reviewer": "Reviewer_eLAA", "summary": "This paper studies the problem of federated recommendation and proposes a new method improving the personalization performance. The authors developed a new additive item embedding model that combines a sparse global embedding with a user-personalized embedding, where the federated recommendation only needs to periodically send the sparse embedding between servers and users, and thus saves the communication cost. The user-personalized embedding is only stored at the user devices and optimized for each user so the personalization performance can be improved. They developed a curriculum to facilitate the training of the two embeddings, which learns the personalized ones at first before adding the sparse global one. This method achieved significant improvements on five benchmark datasets of different domains and sizes. The paper also provides a differential privacy analysis to the proposed approach.", "questions": "1. Can you provide an ablation study of the regularization in Eq2? \n2. \"FedRAP-No: Removes item diversity constraint on C\" --- should \"diversity\" be \"sparsity\"? Is this a typo?\n3. In Figure 7, it seems that each user-personalized matrix is row-sparse with most rows to be all-zeros. How do we understand this row-sparsity? Can you (or did you) take advantage of this row-sparsity to further reduce the communication cost (e.g., by only sending the non-allzero rows)?\n4. How do we justify whether the degradation numbers in Table 1 are large or small? Are there any baselines' degradation results to be compared with?\n\nOverall, I think this is a solid work on an important open problem. Their proposed item embedding model has a novel design successfully addressing the communication and personalization problems in federated recommendation. Their reported improvement (when compared to existing federated recommendation approaches) on six well-known benchmark datasets is impressive and significant, demonstrating the effectiveness. The ablation study and differential privacy analysis are thorough and further strengthened the work. The paper is clear overall but there is still some room for improving the writing/presentation. Some questions above need further clarifications.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Federated recommendation is an important problem that has not been fully explored in the federated learning community. Improving the personalization performance is an open key challenge in the federated recommendation setting.\n2. The proposed additive item embedding model is a novel contribution to the field and addresses two critical challenges in the federated recommendation area, i.e,. reducing communication cost and personalization with knowledge sharing.\n3. The ablation study shows that the curriculum is important to learn the item embedding model.\n4. The performance achieved by the proposed FedRAP is remarkable in Figure 1: on six widely-used benchmarks, it outperforms all the federated recommendation baselines by a substantial margin and largely reduces the gap between federated algorithms and centralized ones---this is nontrivial and worth being highlighted. \n5. The ablation study includes 8 variants of the proposed methods and demonstrates the advantage of most components in the proposed FedRAP. The analysis and experiments regarding differential privacy make their method more trustworthy. \n6. They provided the code, which facilitates reproducible research in the future. I believe this work with the code released can be very interesting to people working in the area of federated recommendation systems.", "weaknesses": "1. The variants in the ablation study introduced in Section 4.5 do not come with intuitive explanations of the motivations for comparisions with them. They are unclear to readers if they do not carefully read the following analysis. \n2. There are some typos and grammar errors which should be corrected.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ElQdWC4XC2", "reviewer": "Reviewer_MAPK", "summary": "Federated Recommendation Systems (FRS) is a promising field, however, existing methods not pay enough attention to personalization . To enhance personalization in the field of FRS, FedRAP proposed by the authors address the challenges of heterogeneity and personalization in FRSs. FedRAP decouples the common knowledge and personalized knowledge by decoupling user embedding to on-server (updated by server-client communication) sparse one and on-client one. Regularization is used to restrict training.", "questions": "None", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. It points out the shortcomings of the existing FRS methods, and gives a complete solution, which is instructive.\n2. The framework is complete, and the paper is clearly expressed.\n3. Theoretical analysis is sufficient. Proofs of error and differential privacy in federated learning are comprehensive.\n4. Limitations about FedRAP are detailed", "weaknesses": "None", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wlSaz7zJEg", "reviewer": "Reviewer_QwLj", "summary": "To overcome the challenge of personalization, the authors introduce the Federated Recommendation with Additive Personalization (FedRAP). This method combines global item insights from FL with local, user-specific perspectives. FedRAP ensures efficient communication by promoting sparser global embeddings and uses two regularizers to balance global and local views. The primary contributions include the introduction of the dual personalization approach, the use of two distinct regularizers.", "questions": "1.\tWhy is the term \"Reconstruction error\" used in Figure 2? What exactly is being 'reconstructed' in the given context?\n2.\tCould the authors delineate the difference between 'heterogeneity' and 'personalization'? As of now, the distinction is not abundantly clear, and the terms seem somewhat interchangeable.\n3.\tThe paper emphasizes the sparse embedding's ability to reduce communication costs, a known limitation of current methods. Why is this specific aspect not experimentally validated or discussed in the results section? Can the authors provide insights or potential experimental outcomes supporting this claim?\n4.\tGiven that \"PerFedRec\" is cited in the related work and seems to be a relevant approach in this domain, why is it omitted from the experimental comparisons? A comparative analysis could provide a clearer perspective on the proposed method's advantages or potential differences.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.\tThe authors have adeptly addressed communication challenges in Federated Learning by advocating for a sparse global view. This practical strategy can potentially alleviate communication bottlenecks.\n2.\tThe emphasis on federated recommendations is commendable, since data privacy is a priority.", "weaknesses": "Clarity and Consistency in Writing: The manuscript requires clarity and consistency checks.\na. In Figure 1's title, \"five dataset\" is mentioned, whereas at the end of Section 1, it states “four dataset”.\nb. There seems to be a mismatch in presentation; the results from Section 4.4 on Page 7 are introduced as early as Page 2 in the introduction. Is there a specific reason behind placing the experimental results in the introductory section?\n\nExplicit Statement of Motivation and Research Question: The introductory section should more explicitly state the motivation and the central research question. While Paragraph 3 introduces federated recommendation, and Paragraph 4 delineates the main contribution, there's a lack of clarity on the necessity of addressing heterogeneity and personalization in Federated Recommendation (FedRec). The central research questions around the importance of personalization in FedRec and the inadequacies of existing methods demand elaboration.\n\nDifferentiating Heterogeneity from Personalization: The distinction between heterogeneity and personalization needs to be clearer. The manuscript suggests two challenges: heterogeneity, which seems to pertain to varying client interests, and personalization. Given their similarities in this context, why are they presented as two separate challenges?\n\nOrganizational Refinements: There's a need for reorganizing the content, especially in Section 3.1 (Problem Formulation). Here, the authors discuss the loss function and the proposed method's entire framework. A more suitable structure might involve mathematically introducing the problem in the Problem Formulation and discussing the method's framework in a separate section.\n\nClarification on 'Reconstruction Error': Figure 2 illustrates the \"reconstruction error\". As per my understanding, it seems to measure the discrepancy between actual user ratings and predicted ones. The term \"reconstruction error\" suggests that something undergoes reconstruction. Can the authors clarify this?\n\nQuestioning Novelty: The paper's primary innovation appears to be the maintenance of a user-specific item embedding matrix, D(i), for every user, i. Given that local interactions are usually sparse, which motivates federated training, the uniqueness of this approach is incremental. The two regularizers are essentially the normalization of two embedding tables. While differential privacy is prevalent in federated learning, its application here doesn't seem groundbreaking.\n\nComparative Analysis with Relevant Works: The manuscript cites PerFedRec [1] in the related works but doesn't present a comparative analysis in the experiments. It would be insightful for the authors to contrast their work with PerFedRec. Moreover, considering the work on PerFedRec++ [2] could be beneficial, even if it's a recent arXiv publication.\n\nReferences:\n[1] Luo, S., Xiao, Y., & Song, L. (2022, October). Personalized federated recommendation via joint representation learning, user clustering, and model adaptation. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 4289-4293).\n[2] Luo, S., Xiao, Y., Zhang, X., Liu, Y., Ding, W., & Song, L. (2023). PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training. arXiv preprint arXiv:2305.06622.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (poster)