Robust Federated Recommendation System

2020/06/15 by Chen Chen, Jingfeng Zhang, Chen, Chen +7
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Privacy-Preserving Technologies in Data #Recommender Systems and Techniques #Stochastic Gradient Optimization Techniques

paper · pdf · doi:10.48550/arxiv.2006.08259

openalex publication_date 2020/06/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning attacks that can degrade their performance. In this paper, we develop a novel federated recommendation technique that is robust against the poisoning attack where Byzantine clients prevail. We argue that the key to Byzantine detection is monitoring of gradients of the model parameters of clients. We then propose a robust learning strategy where instead of using model parameters, the central server computes and utilizes the gradients to filter out Byzantine clients. Theoretically, we justify our robust learning strategy by our proposed definition of Byzantine resilience. Empirically, we confirm the efficacy of our robust learning strategy employing four datasets in a federated recommendation system.

Citations

• BPR: Bayesian Personalized Ranking from Implicit Feedback
• Phocas: dimensional Byzantine-resilient stochastic gradient descent
• How To Backdoor Federated Learning
• On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
• Learning Disentangled Representations for Recommendation
• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
• Robust Aggregation for Federated Learning
• Learning to Detect Malicious Clients for Robust Federated Learning

Related

• Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective
• FedGuard: A Diverse-Byzantine-Robust Mechanism for Federated Learning with Major Malicious Clients
• Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
• FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
• AFLGuard: Byzantine-robust Asynchronous Federated Learning