FedSub: Introducing Class-aware Subnetworks Fusion to Enhance Personalized Federated Learning

TL;DR

FedSub introduces class-aware model updates and subnetwork fusion in federated learning, effectively balancing personalization and generalization in highly heterogeneous data environments, leading to improved convergence and classification accuracy.

Contribution

The paper presents FedSub, a novel method that uses data prototypes and subnetwork fusion to enhance personalized federated learning.

Findings

Outperforms state-of-the-art methods in real-world scenarios.

Achieves faster convergence in heterogeneous data settings.

Improves classification performance in human activity recognition and mobile health applications.

Abstract

Personalized Federated Learning aims at addressing the challenges of non-IID data in collaborative model training. However, existing methods struggle to balance personalization and generalization, often oversimplifying client similarities or relying too heavily on global models. In this paper, we propose FedSub, a novel approach that introduces class-aware model updates based on data prototypes and model subnetworks fusion to enhance personalization. Prototypes serve as compact representations of client data for each class, clustered on the server to capture label-specific similarities among the clients. Meanwhile, model subnetworks encapsulate the most relevant components to process each class and they are then fused on the server based on the identified clusters to generate fine-grained, class-specific, and highly personalized model updates for each client. Experimental results in three real-world scenarios with high data heterogeneity in human activity recognition and mobile health applications demonstrate the effectiveness of FedSub with respect to state-of-the-art methods to achieve fast convergence and high classification performance.