Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation

• URL: http://arxiv.org/abs/2508.10401v1
• Date: Thu, 14 Aug 2025 07:03:39 GMT
• Title: Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
• Authors: Liang Qu, Jianxin Li, Wei Yuan, Penghui Ruan, Yuhui Shi, Hongzhi Yin,
• Abstract summary: Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data.<n>Most existing FedRS frameworks adopt fully random client selection strategy in each training round.<n>We propose ProxyRL-FRS, a proxy model-guided reinforcement learning framework tailored for client selection in federated recommendation.
• Score: 41.93915547763831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data. By keeping data local and relying on a central server to coordinate training across distributed clients, FedRSs protect user privacy while collaboratively learning global models. However, most existing FedRS frameworks adopt fully random client selection strategy in each training round, overlooking the statistical heterogeneity of user data arising from diverse preferences and behavior patterns, thereby resulting in suboptimal model performance. While some client selection strategies have been proposed in the broader federated learning literature, these methods are typically designed for generic tasks and fail to address the unique challenges of recommendation scenarios, such as expensive contribution evaluation due to the large number of clients, and sparse updates resulting from long-tail item distributions. To bridge this gap, we propose ProxyRL-FRS, a proxy model-guided reinforcement learning framework tailored for client selection in federated recommendation. Specifically, we first introduce ProxyNCF, a dual-branch model deployed on each client, which augments standard Neural Collaborative Filtering with an additional proxy model branch that provides lightweight contribution estimation, thus eliminating the need for expensive per-round local training traditionally required to evaluate a client's contribution. Furthermore, we design a staleness-aware SA reinforcement learning agent that selects clients based on the proxy-estimated contribution, and is guided by a reward function balancing recommendation accuracy and embedding staleness, thereby enriching the update coverage of item embeddings. Experiments conducted on public recommendation datasets demonstrate the effectiveness of ProxyRL-FRS.

Related papers

• FedPPA: Progressive Parameter Alignment for Personalized Federated Learning [0.9931624906346306]
  Federated Learning (FL) is designed as a decentralized, privacy-preserving machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data.<n>In real-world scenarios, clients often have heterogeneous computational resources and hold non-independent and identically distributed data (non-IID), which poses significant challenges during training.<n>We propose Progressive Alignment (FedPPA), which progressively aligns the weights of common layers across clients with the global model's weights.<n>Experiments on three image classification datasets, including MNIST, FMNIST, and CIFAR-10, demonstrate that FedPPA consistently outperforms existing FL algorithms,
  arXiv  Detail & Related papers  (2025-10-16T14:03:05Z)
• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private.<n>We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• Co-clustering for Federated Recommender System [33.70723179405055]
  Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. The presence of statistical heterogeneity in FRS, commonly observed due to personalized decision-making patterns, can pose challenges. We propose CoFedRec, a novel Co-clustering Federated Recommendation mechanism.
  arXiv  Detail & Related papers  (2024-11-03T21:32:07Z)
• Efficient and Robust Regularized Federated Recommendation [52.24782464815489]
  The recommender system (RSRS) addresses both user preference and privacy concerns. We propose a novel method that incorporates non-uniform gradient descent to improve communication efficiency. RFRecF's superior robustness compared to diverse baselines.
  arXiv  Detail & Related papers  (2024-11-03T12:10:20Z)
• PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender System [15.589541738576528]
  Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations. In FedRecs, users keep their data locally and only contribute their local collaborative information by uploading model parameters to a central server. We propose a novel federated recommendation framework, PDC-FRS. Specifically, we design a privacy-preserving data contribution mechanism that allows users to share their data with a differential privacy guarantee.
  arXiv  Detail & Related papers  (2024-09-12T06:13:07Z)
• Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated Learning [8.842172558292027]
  Federated Learning (FL) has been proposed as a privacy-preserving solution for distributed machine learning.<n>Recent studies have shown that it is susceptible to membership inference attacks (MIA), which can compromise the privacy of client data.
  arXiv  Detail & Related papers  (2023-11-29T15:54:15Z)
• Blockchain-based Optimized Client Selection and Privacy Preserved Framework for Federated Learning [2.4201849657206496]
  Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients. With this feature, federated learning is considered a secure solution for data privacy issues. We proposed the blockchain-based optimized client selection and privacy-preserved framework.
  arXiv  Detail & Related papers  (2023-07-25T01:35:51Z)
• Optimizing Server-side Aggregation For Robust Federated Learning via Subspace Training [80.03567604524268]
  Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning systems. We propose SmartFL, a generic approach that optimize the server-side aggregation process. We provide theoretical analyses of the convergence and generalization capacity for SmartFL.
  arXiv  Detail & Related papers  (2022-11-10T13:20:56Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
  We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives. We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
  arXiv  Detail & Related papers  (2020-12-15T19:30:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.