Blockchain-Integrated Federated Learning for Cross-Institutional Medical Research

Abstract

The rapid digital transformation of healthcare has generated enormous volumes of heterogeneous medical data from hospitals, research laboratories, diagnostic centers, and wearable healthcare devices. These distributed datasets present unprecedented opportunities for developing intelligent clinical decision support systems using artificial intelligence (AI). However, strict privacy regulations, institutional policies, and cybersecurity concerns significantly restrict the sharing of sensitive patient information across organizations. Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables collaborative model training without exchanging raw medical data. While conventional FL offers inherent advantages, it is subjected to challenges including centralized aggregation and impacts from malicious participants such as participating adversarial agents performing model poisoning attacks or moving around in a vehicular network leading to non-transparent trust management. Decentralization consensus, immutability, traceability and tamper-proof transaction record capabilities of blockchain technology is an excellent complement to federated learning. This paper proposes a novel blockchain based federated learning framework for non-sensitive cross-institutional medical data research. We present a solution which integrates decentralized blockchain networks with federated model aggregation offering secure parameter exchange, transparent participant validation, tamper-resistant audit trails and increased trust between collaborating healthcare institutions. Smart contracts provide data security, model privacy and automated protocol of database access as well as a rudimentary sense of control to prevent forgery. The framework achieves substantial gains in privacy preservation, collaborative intelligence, system scalability, and adversarial attack robustness. This integration enables safe, reliable and scalable collaborative medical intelligence for efficient multi-institutional medical research, accelerating AI-based health innovations, and maintaining compliance with recent healthcare privacy regulations.