Title: Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem
Venue: NeurIPS 2025
Accepted: yes
Keywords: Federated Learning

Recommendation System
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

comments: 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.

STRENGTHS AND WEAKNESSES:
**Pros:**

(+) 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.

(+) The proposed method is easy to follow and seems technically sound.

(+) Extensive experimental evaluation convincingly supports the effectiveness of the proposed method.

**Cons:**

(-) The bridge function’s linear design may limit complex preference mappings; exploring nonlinear variants might boost performance.

(-) 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?

QUESTIONS:
1. For overlapping items, how does FR-JVE resolve conflicting preferences from different subsidiaries?

2. How does performance degrade with increasing numbers of subsidiaries?

3. 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
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-06-30

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The authors focused on a novel and practical problem where different subsidiaries need to collaborate on recommendation systems with partially overlapping user/item sets.
2. The proposed method is technically sound while rating preference distillation with frozen user gradients can avoid raw data exposure.
3. Evaluations are sufficient and rigorous across various settings.
4. The paper is well-organized and easy to read.

Weaknesses:
1. 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.
2. 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.
3. Experiments use synthetic subsidiary splits with Dirichlet distribution. Validation on real joint venture data is lacking.
4. 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.

QUESTIONS:
1. Why not incorporate lightweight DP on aggregated preferences for stronger privacy guarantees?
2. How would FR-JVE handle subsidiaries with highly skewed data distributions?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}