Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

2026-09-07

AI Summary

本文提出了一种鲁棒的去中心化个性化联邦学习方法R-DPFL，通过稳健的邻域方向估计和基于历史的更新趋势预测来减少拜占庭攻击的影响。

Abstract

This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks via robust neighborhood direction estimation and history-based update trend prediction, rather than purely aggregating client models as in the existing work. In R-DPFL, each client first computes the current-round model update by aggregating the received neighborhood update vectors. It then predicts what this update should be based on its historical values and local model changes. Finally, R-DPFL computes the difference between these two quantities, adaptively clips this difference, and adds it to the local update. We prove convergence of the learning process through rigorous analysis and show that honest clients maintain stable personalized descent dynamics under Byzantine neighbor perturbations without requiring consensus among neighboring models. Extensive experiments on CIFAR-10 demonstrate that RDPFL consistently outperforms state-of-the-art decentralized and personalized federated learning baselines under heterogeneous and adversarial settings.

Problem

• Byzantine attacks
• personalized federated learning
• decentralized

Innovation

• Robust Decentralized Personalized Federated Learning
• Byzantine Attacks
• Neighborhood Direction Estimation
• History-based Update Trend Prediction
• Adaptive Clipping