Federated Learning With Deep Neural Networks: A Privacy-Preserving Approach to Enhanced ECG Classification.

Area of Science:

• Artificial Intelligence in Medicine
• Cardiology
• Machine Learning for Healthcare

Background:

• Increasing data privacy regulations necessitate novel approaches for medical data analysis.
• Diagnosing cardiac abnormalities from electrocardiogram (ECG) data requires robust and privacy-preserving machine learning models.
• Traditional centralized training models require sharing sensitive patient data, posing privacy risks.

Purpose of the Study:

• To evaluate the efficacy of federated learning (FL) for diagnosing cardiac abnormalities using deep residual networks on ECG data.
• To compare FL performance against centralized training (with data sharing) and isolated training scenarios.
• To assess the generalizability of FL models across diverse, unseen patient data.

Main Methods:

• Utilized publicly available ECG data from the PhysioNet/Computing in Cardiology Challenge 2021.
• Implemented and compared three federated learning algorithms for deep residual networks.
• Benchmarked FL performance against centralized training and isolated local training models.

Main Results:

• Federated learning significantly outperformed ECG classifiers trained in isolation.
• Globally trained FL models, when fine-tuned locally, surpassed non-collaborative approaches.
• FL achieved performance comparable to centralized training on out-of-distribution data, demonstrating strong generalization.

Conclusions:

• Federated learning offers a viable, privacy-preserving solution for collaborative ECG analysis in healthcare.
• FL models learn generalizable cardiac features that can be adapted to specific institutional datasets.
• This approach effectively addresses data privacy concerns while enabling robust cardiac abnormality diagnosis.