Healthcare data privacy and collaborative machine learning represent two critical challenges that have historically been at odds with each other. The advent of federated learning (FL) combined with edge computing technologies provides a revolutionary approach to address these challenges simultaneously. This research presents a comprehensive framework for privacy-preserving medical diagnosis using federated learning deployed on edge devices. The proposed system enables multiple healthcare institutions to collaboratively train diagnostic models without sharing sensitive patient data, while maintaining real-time inference capabilities through edge computing platforms. Our approach integrates advanced privacy-preserving techniques including homomorphic encryption, differential privacy, and secure multi-party computation with optimized edge deployment on NVIDIA Jetson platforms. The framework demonstrates significant potential for transforming medical AI through secure, efficient, and collaborative learning paradigms that comply with healthcare regulations while achieving superior diagnostic accuracy. Experimental results show that federated learning models achieve 91-98% accuracy across medical imaging datasets while maintaining strict privacy guarantees and reducing communication overhead by 25-60% compared to traditional approaches.
Chintala et al. (Thu,) studied this question.