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.
View on arXiv@article{soltany2025_2412.11408,
title={ Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training },
author={ Milad Soltany and Farhad Pourpanah and Mahdiyar Molahasani and Michael Greenspan and Ali Etemad },
journal={arXiv preprint arXiv:2412.11408},
year={ 2025 }
}