Federated Governance Architecture for Collaborative Healthcare AI

Federated Learning (FL) has emerged as a groundbreaking decentralized machine learning (ML) approach that enables multiple organizations to collaboratively train models without sharing sensitive data. However, applying FL in healthcare poses several challenges, such as data heterogeneity, communication overhead, adversarial attacks, regulatory compliance, real-time adaptability, trust among participants, and feature harmonization. This paper explores the current FL architecture in healthcare applications and proposes a Federated Governance Architecture (FGA) to address these challenges. The FGA provides robust governance, legal frameworks, and technological infrastructure to enforce privacy, security, and data harmonization standards across a distributed system. It includes an auditable process for data harmonization and secure communication between participants, ensuring the secure and effective management of the FL Global Model (GM). This architecture aims to enhance ML model performance through secure collaboration while preserving local data privacy and ensuring compliance with regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the California Confidentiality of Medical Information Act (CMIA).