Governing Federated Learning in Healthcare: A Transaction Cost Economics and Resource-Based View Analysis

Federated learning provides a promising avenue for collaborative machine learning without sharing raw data, which can be particularly appealing in clinical settings due to the inherently sensitive nature of medical data. However, existing research on federated learning in the healthcare sector focuses on the technical architecture and implementation challenges. Given the complex regulatory landscape in healthcare, federated learning governance is a critical yet under-explored necessity to support sustainable long-term adoption. This paper applies transaction cost economics and the resource-based view to analyze the organizational pressures, risks, and assets involved in federated learning and develop a governance framework that addresses recurrent issues in the existing literature. The resultant five-pillar framework is intended to provide actionable guidance for healthcare organizations seeking to implement federated learning in a way that is operationally sustainable and clinically sound.