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arXiv AI

Influence-Oriented Personalized Federated Learning

Influence-Oriented Personalized Federated Learning (FedC^2I) introduces a framework that quantifies both client-level and class-level influence to enable adaptive parameter aggregation in federated learning. By modeling inter-client influence through influence vectors and matrices, FedC^2I allows clients to selectively acquire knowledge from similar peers and guides the aggregation of feature representations and classifiers. Experiments under non-IID settings show that FedC^2I outperforms existing federated learning methods in effectiveness, robustness, and interpretability.

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