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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2412.11408 |
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| _version_ | 1866929705389654016 |
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| author | Soltany, Milad Pourpanah, Farhad Molahasani, Mahdiyar Greenspan, Michael Etemad, Ali |
| author_facet | Soltany, Milad Pourpanah, Farhad Molahasani, Mahdiyar Greenspan, Michael Etemad, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11408 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training Soltany, Milad Pourpanah, Farhad Molahasani, Mahdiyar Greenspan, Michael Etemad, Ali Machine Learning Artificial Intelligence 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. |
| title | Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.11408 |