Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data

• URL: http://arxiv.org/abs/2509.07198v1
• Date: Mon, 08 Sep 2025 20:24:40 GMT
• Title: Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
• Authors: Yiyue Chen, Usman Akram, Chianing Wang, Haris Vikalo,
• Abstract summary: Fed-REACT is a federated learning framework designed for heterogeneous and evolving client data.<n>We provide a theoretical analysis of the representation learning stage, and empirically demonstrate that Fed-REACT achieves superior accuracy and robustness on real-world datasets.
• Score: 19.33095080645165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by the high resource costs and privacy concerns associated with centralized machine learning, federated learning (FL) has emerged as an efficient alternative that enables clients to collaboratively train a global model while keeping their data local. However, in real-world deployments, client data distributions often evolve over time and differ significantly across clients, introducing heterogeneity that degrades the performance of standard FL algorithms. In this work, we introduce Fed-REACT, a federated learning framework designed for heterogeneous and evolving client data. Fed-REACT combines representation learning with evolutionary clustering in a two-stage process: (1) in the first stage, each client learns a local model to extracts feature representations from its data; (2) in the second stage, the server dynamically groups clients into clusters based on these representations and coordinates cluster-wise training of task-specific models for downstream objectives such as classification or regression. We provide a theoretical analysis of the representation learning stage, and empirically demonstrate that Fed-REACT achieves superior accuracy and robustness on real-world datasets.

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