Reads: A Personalized Federated Learning Framework With Fine-Grained Layer Aggregation and Decentralized Clustering
Haoyu Fu, Fengsen Tian, Guoqiang Deng, Lingyu Liang, Xinglin Zhang · IEEE Transactions on Mobile Computing · 2025
The heterogeneity of local data and client performance, along with real-world system risks, is driving the evolution of federated learning (FL) towards personalized, model-heterogeneous, and decentralized approaches. However, due to the differing structures of heterogeneous models, it is hard to use them to identify clients with similar data distributions and further enhance the personalization of local models. Therefore, how to deal with data heterogeneity to obtain superior personalized local models for clients, while simultaneously addressing model heterogeneity and system risks is a challenging problem. In this paper, we propose a novel personalized FL framework with fine-gRained layEr aggregAtion andDecentralized cluStering (${\sf Reads}$), which integrates four key components: (1) deep mutual learning with privacy guarantee for model training and privacy preservation, (2) fine-grained layer similarity computation among heterogeneous model layers, (3) fully decentralized clustering for soft clustering of clients based on layer similarities, and (4) personalized layer aggregation for capturing common knowledge from other clients. Through${\sf Reads}$, clients obtain personalized models that accommodate model heterogeneity, while the system ensures robustness against a single point of failure. Extensive experiments demonstrate the efficacy of${\sf Reads}$in achieving these goals.