用联邦学习微调医学时间序列大模型，保护隐私同时提升预测效果
Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting
- 通过联邦学习在多客户端上微调时间序列大模型，避免原始数据共享
- 实验证明在同质数据下效果接近集中式训练，异质数据下性能下降
- 适合医疗领域需严守数据隐私的场景，尤其关注跨设备数据差异问题
联邦学习（FL）是一种去中心化机器学习方法，允许多个设备或服务器协作训练模型而不共享原始数据，从而保障数据隐私。该方法因隐私保护特性在学术界和工业界受到广泛关注，尤其适用于医疗领域中受严格法规保护的数据。一个相对未被探索的方向是使用联邦学习微调时间序列基础模型（FMs），有望在克服数据限制的同时保持隐私。本文采用不同联邦学习技术，对心电图（ECG）和阻抗心动图（ICG）数据上的时间序列基础模型进行微调，并考察了多种场景下不同数据异质性配置带来的挑战。实验结果表明，尽管联邦学习在时间序列预测任务中对基础模型微调具有潜力，但其有效性取决于客户端间数据分布的一致性。研究揭示了在基础模型微调中应用联邦学习的权衡关系。
原文摘要 · Abstract (English)
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.
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