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

Description

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.

Code

FedAPA Code is available at: FedAPA Code

Convergence Analysis

To access our proof of convergence, visit: Convergence Analysis

Citations

If you use our resource in your research, please cite our paper on IJCAI’25:

ACM Reference Format:

Yuxia Sun, Aoxiang Sun, Siyi Pan, Zhixiao Fu, and Jingcai Guo. FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data. In Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI '25), August 16–22, 2025, Montreal, Canada.