FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems

• URL: http://arxiv.org/abs/2310.20193v1
• Date: Tue, 31 Oct 2023 05:36:53 GMT
• Title: FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
• Authors: Lin Wang, Zhichao Wang, Xi Leng, Xiaoying Tang
• Abstract summary: FedRec+ is an ensemble framework for federated recommendation systems. It enhances privacy and reduces communication costs for edge users. Experimental results demonstrate the state-of-the-art performance of FedRec+.
• Score: 15.463595798992621
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protecting privacy by avoiding data exchange between clients and servers, it has been shown that the server can infer user ratings based on updated non-zero gradients obtained from two consecutive rounds of user-uploaded gradients. Moreover, federated recommendation systems (FRS) face the challenge of heterogeneity, leading to decreased recommendation performance. In this paper, we propose FedRec+, an ensemble framework for FRS that enhances privacy while addressing the heterogeneity challenge. FedRec+ employs optimal subset selection based on feature similarity to generate near-optimal virtual ratings for pseudo items, utilizing only the user's local information. This approach reduces noise without incurring additional communication costs. Furthermore, we utilize the Wasserstein distance to estimate the heterogeneity and contribution of each client, and derive optimal aggregation weights by solving a defined optimization problem. Experimental results demonstrate the state-of-the-art performance of FedRec+ across various reference datasets.

Related papers

• 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)
• User Consented Federated Recommender System Against Personalized Attribute Inference Attack [55.24441467292359]
  We propose a user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent.
  arXiv  Detail & Related papers  (2023-12-23T09:44:57Z)
• Differentially Private Federated Clustering over Non-IID Data [59.611244450530315]
  clustering clusters (FedC) problem aims to accurately partition unlabeled data samples distributed over massive clients into finite clients under the orchestration of a server. We propose a novel FedC algorithm using differential privacy convergence technique, referred to as DP-Fed, in which partial participation and multiple clients are also considered. Various attributes of the proposed DP-Fed are obtained through theoretical analyses of privacy protection, especially for the case of non-identically and independently distributed (non-i.i.d.) data.
  arXiv  Detail & Related papers  (2023-01-03T05:38:43Z)
• FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation [95.85026305874824]
  We introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency.
  arXiv  Detail & Related papers  (2022-12-14T13:57:01Z)
• FedGRec: Federated Graph Recommender System with Lazy Update of Latent Embeddings [108.77460689459247]
  We propose a Federated Graph Recommender System (FedGRec) to mitigate privacy concerns. In our system, users and the server explicitly store latent embeddings for users and items, where the latent embeddings summarize different orders of indirect user-item interactions. We perform extensive empirical evaluations to verify the efficacy of using latent embeddings as a proxy of missing interaction graph.
  arXiv  Detail & Related papers  (2022-10-25T01:08:20Z)
• FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning [91.74206675452888]
  We propose a novel method FedFM, which guides each client's features to match shared category-wise anchors. To achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, where clients communicate with server with fewer synchronization times and communication bandwidth costs.
  arXiv  Detail & Related papers  (2022-10-14T08:11:34Z)
• Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis [14.545746907150436]
  FedReID implements federated learning, an emerging distributed training method, to person ReID. FedReID preserves data privacy by aggregating model updates, instead of raw data, from clients to a central server.
  arXiv  Detail & Related papers  (2022-05-24T15:20:32Z)
• A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations [25.484225182093947]
  Privacy-preserving recommendations are gaining momentum due to concerns over user privacy and data security. FedRec algorithms have been proposed to realize personalized privacy-preserving recommendations. This paper presents FLMV-DSSM, a generic content-based federated multi-view recommendation framework.
  arXiv  Detail & Related papers  (2020-08-25T04:19:40Z)

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.