FedRIR: Rethinking Information Representation in Federated Learning

摘要

Mobile and Web-of-Things (WoT) devices at the network edge generate vast amounts of data for machine learning applications, yet privacy concerns hinder centralized model training. Federated Learning (FL) allows clients (devices) to collaboratively train a shared model coordinated by a central server without transferring private data. However, inherent statistical heterogeneity among clients presents challenges, often leading to a dilemma between clients' need for personalized local models and the server's goal of building a generalized global model. Existing FL methods typically prioritize either global generalization or local personalization, resulting in a trade-off between these objectives and limiting the full potential of diverse client data. To address this challenge, we propose a novel framework that enhances both global generalization and local personalization by Rethinking Information Representation in the Federated learning process (FedRIR). Specifically, we introduce Masked Client-Specific Learning (MCSL), which isolates and extracts fine-grained client-specific features tailored to each client's unique data characteristics, thereby enhancing personalization. Meanwhile, the Information Distillation Module (IDM) refines global shared features by filtering out redundant client-specific information, resulting in a purer and more robust global representation that enhances generalization. By integrating refined global features with isolated client-specific features, we construct enriched representations that effectively capture both global patterns and local nuances, thereby improving the performance of downstream tasks on the client. Extensive experiments on diverse datasets demonstrate that FedRIR significantly outperforms state-of-the-art FL methods, achieving up to a 3.93% improvement in accuracy while ensuring robustness and stability in heterogeneous environments. The code is publicly available at https://github.com/Deep-Imaging-Group/FedRIR.