Robust Federated Learning on Edge Devices with Domain Heterogeneity

• URL: http://arxiv.org/abs/2505.10128v1
• Date: Thu, 15 May 2025 09:53:14 GMT
• Title: Robust Federated Learning on Edge Devices with Domain Heterogeneity
• Authors: Huy Q. Le, Latif U. Khan, Choong Seon Hong,
• Abstract summary: Federated Learning (FL) allows collaborative training while ensuring data privacy across distributed edge devices.<n>We introduce a new framework to address this challenge by improving the generalization ability of the FL global model.<n>We introduce FedAPC, a prototype-based FL framework designed to enhance feature diversity and model robustness.
• Score: 13.362209980631876
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Federated Learning (FL) allows collaborative training while ensuring data privacy across distributed edge devices, making it a popular solution for privacy-sensitive applications. However, FL faces significant challenges due to statistical heterogeneity, particularly domain heterogeneity, which impedes the global mode's convergence. In this study, we introduce a new framework to address this challenge by improving the generalization ability of the FL global model under domain heterogeneity, using prototype augmentation. Specifically, we introduce FedAPC (Federated Augmented Prototype Contrastive Learning), a prototype-based FL framework designed to enhance feature diversity and model robustness. FedAPC leverages prototypes derived from the mean features of augmented data to capture richer representations. By aligning local features with global prototypes, we enable the model to learn meaningful semantic features while reducing overfitting to any specific domain. Experimental results on the Office-10 and Digits datasets illustrate that our framework outperforms SOTA baselines, demonstrating superior performance.

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