FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data

• URL: http://arxiv.org/abs/2502.07456v2
• Date: Sat, 15 Feb 2025 15:40:47 GMT
• Title: FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data
• Authors: Yuxia Sun, Aoxiang Sun, Siyi Pan, Zhixiao Fu, Jingcai Guo,
• Abstract summary: FedAPA is a novel PFL method featuring a server-side, gradient-based adaptive aggregation strategy to generate personalized models. FedAPA guarantees theoretical convergence and achieves superior accuracy and computational efficiency compared to 10 PFL competitors across three datasets.
• Score: 5.906966694759679
• License:
• Abstract: Personalized federated learning (PFL) tailors models to clients' unique data distributions while preserving privacy. However, existing aggregation-weight-based PFL methods often struggle with heterogeneous data, facing challenges in accuracy, computational efficiency, and communication overhead. We propose FedAPA, a novel PFL method featuring a server-side, gradient-based adaptive aggregation strategy to generate personalized models, by updating aggregation weights based on gradients of client-parameter changes with respect to the aggregation weights in a centralized manner. FedAPA guarantees theoretical convergence and achieves superior accuracy and computational efficiency compared to 10 PFL competitors across three datasets, with competitive communication overhead.

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