Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Biomedical Informatics

Background:

• Federated Learning (FL) enables decentralized model training without raw data sharing, preserving privacy.
• Privacy concerns and regulations in the medical domain limit data availability for AI model development.
• Foundation Models (FMs) show promise for complex tasks like time series forecasting.

Purpose of the Study:

• To investigate the application of Federated Learning (FL) for fine-tuning Foundation Models (FMs) on time series forecasting tasks.
• To evaluate the efficacy of FL in preserving data privacy when using sensitive medical data like Electrocardiogram (ECG) and Impedance Cardiography (ICG).
• To analyze the impact of data heterogeneity on FL performance in this context.

Main Methods:

• Fine-tuned time series Foundation Models (FMs) using Electrocardiogram (ECG) and Impedance Cardiography (ICG) data.
• Employed various Federated Learning (FL) techniques for decentralized training.
• Examined different data heterogeneity configurations and their influence on FL performance.

Main Results:

• Federated Learning (FL) demonstrated effectiveness in fine-tuning Foundation Models (FMs) for time series forecasting with private medical data.
• The performance benefits of FL were contingent upon the data distribution across participating clients (data heterogeneity).
• Identified and discussed key challenges and trade-offs associated with applying FL to FM fine-tuning in this domain.

Conclusions:

• Federated Learning (FL) is a viable approach for privacy-preserving fine-tuning of Foundation Models (FMs) for time series forecasting using medical data.
• Data distribution across clients is a critical factor influencing the success and efficacy of FL.
• Further research is needed to address the challenges posed by data heterogeneity in FL for medical time series analysis.