Distributed Ledger-Enabled Cooperative Learning Architecture Ensuring Confidential Healthcare Analytics

Abstract

Healthcare AI systems put a lot of importance on keeping medical data private because it is very sensitive. AI-driven diagnostic models could help doctors make better decisions, but they need a lot of different patient data sets, which are often kept separate from each other at different hospitals. Federated Learning (FL) is a decentralised way to solve this problem by letting multiple people train a model together without sharing data in one place. But conventional FL frameworks continue to encounter challenges related to trust, transparency, and data integrity. This paper puts forth a Blockchain-Enabled Federated Learning Framework to facilitate secure, privacy-preserving, and auditable medical diagnosis across decentralised healthcare systems. This system uses blockchain's unchangeable nature and smart contract features to make sure that model updates can't be changed, contributions can be tracked, and trust between the entities involved is higher. This combination makes AI-driven diagnostics possible without putting patient privacy, regulatory compliance, or institutional integrity at risk.