Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani, Michael A. Greenspan, Ali Etemad
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.
BibTeX
@inproceedings{icassp2025_federateddomaing,
title = {Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training},
author = {Milad Soltany and Farhad Pourpanah and Mahdiyar Molahasani and Michael A. Greenspan and Ali Etemad},
booktitle = {ICASSP 2025},
year = {2025}
}