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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.09744 |
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| _version_ | 1866929715287162880 |
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| author | Ali, Mahad Lisle, Curtis Moore, Patrick W. Barkouki, Tammer Kirkwood, Brian J. Brattain, Laura J. |
| author_facet | Ali, Mahad Lisle, Curtis Moore, Patrick W. Barkouki, Tammer Kirkwood, Brian J. Brattain, Laura J. |
| contents | Federated Learning (FL) provides a decentralized machine learning approach, where multiple devices or servers collaboratively train a model without sharing their raw data, thus enabling data privacy. This approach has gained significant interest in academia and industry due to its privacy-preserving properties, which are particularly valuable in the medical domain where data availability is often protected under strict regulations. A relatively unexplored area is the use of FL to fine-tune Foundation Models (FMs) for time series forecasting, potentially enhancing model efficacy by overcoming data limitation while maintaining privacy. In this paper, we fine-tuned time series FMs with Electrocardiogram (ECG) and Impedance Cardiography (ICG) data using different FL techniques. We then examined various scenarios and discussed the challenges FL faces under different data heterogeneity configurations. Our empirical results demonstrated that while FL can be effective for fine-tuning FMs on time series forecasting tasks, its benefits depend on the data distribution across clients. We highlighted the trade-offs in applying FL to FM fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09744 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting Ali, Mahad Lisle, Curtis Moore, Patrick W. Barkouki, Tammer Kirkwood, Brian J. Brattain, Laura J. Machine Learning Cryptography and Security Federated Learning (FL) provides a decentralized machine learning approach, where multiple devices or servers collaboratively train a model without sharing their raw data, thus enabling data privacy. This approach has gained significant interest in academia and industry due to its privacy-preserving properties, which are particularly valuable in the medical domain where data availability is often protected under strict regulations. A relatively unexplored area is the use of FL to fine-tune Foundation Models (FMs) for time series forecasting, potentially enhancing model efficacy by overcoming data limitation while maintaining privacy. In this paper, we fine-tuned time series FMs with Electrocardiogram (ECG) and Impedance Cardiography (ICG) data using different FL techniques. We then examined various scenarios and discussed the challenges FL faces under different data heterogeneity configurations. Our empirical results demonstrated that while FL can be effective for fine-tuning FMs on time series forecasting tasks, its benefits depend on the data distribution across clients. We highlighted the trade-offs in applying FL to FM fine-tuning. |
| title | Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2502.09744 |