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arXiv · 2412.11408

Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training

Milad Soltany·Farhad Pourpanah·Mahdiyar Molahasani·Michael Greenspan·Ali Etemad
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Abstract

In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data heterogeneity within a federated learning framework. FedSB utilizes label smoothing at the client level to prevent overfitting to domain-specific features, thereby enhancing generalization capabilities across diverse domains when aggregating local models into a global model. Additionally, FedSB incorporates a decentralized budgeting mechanism which balances training among clients, which is shown to improve the performance of the aggregated global model. Extensive experiments on four commonly used multi-domain datasets, PACS, VLCS, OfficeHome, and TerraInc, demonstrate that FedSB outperforms competing methods, achieving state-of-the-art results on three out of four datasets, indicating the effectiveness of FedSB in addressing data heterogeneity.

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Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani, Michael Greenspan, Ali Etemad. 2024-12-16. Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training. https://arxiv.org/abs/2412.11408

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