A Governance Framework for Federated Learning Projects in Healthcare

Area of Science:

• Computer Science
• Information Technology
• Data Privacy

Background:

• Federated Learning (FL) adoption is increasing.
• A robust governance framework is essential for safe and compliant FL deployment.
• Existing privacy regulations pose challenges for FL implementation.

Purpose of the Study:

• To present a novel governance framework for Federated Learning projects.
• To demonstrate the practical application of governance agreements within FL.
• To assess the compatibility of FL with current privacy regulations.

Main Methods:

• Development of a comprehensive governance framework tailored for FL.
• Analysis of the frequency and context of applying governance agreements in FL projects.
• Evaluation of the framework's effectiveness in ensuring regulatory compliance.

Main Results:

• The proposed governance framework facilitates the safe implementation of Federated Learning.
• Agreements within the framework were applied consistently across projects.
• Federated Learning, when governed, can operate within existing privacy regulations.

Conclusions:

• Federated Learning is not a method to bypass privacy laws.
• The developed governance framework provides a viable pathway for implementing FL compliantly.
• Effective governance is key to leveraging FL's benefits while adhering to privacy standards.