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Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

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 quant

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