Evolutionary Medical Data Modeling and Sharing via Federated Learning Over Sharded Blockchain

Abstract

Linking medical data silos for medical model learning and sharing makes for better healthcare for humanity. Before that, two critical issues must be solved, i.e., patient privacy protection and data contributors’ rights and interests. This paper proposes an evolutionary medical data modeling and sharing (EMDMS) framework. Specifically, EMDMS adopts a federated learning scheme to coordinate the decentralized medical model learning and model aggregation without the leakage of raw data. A dual-loop federated learning mechanism with a tailored control strategy is developed for the realization of evolutionary model learning with the consideration of the ever-growing medical data. Then, a long-term pricing and revenue distribution strategy is designed for evolutionary model sharing, thus to make the medical model self-growth. It not only ensures fair benefits for data contributors but also enables low-cost sharing of models for public welfare. EMDMS runs on the sharded blockchain to support parallel tasks where dedicated smart contracts are implemented for EMDMS to guarantee security and trustworthiness. A prototype system with simulations on the Fed-ISIC2019 dataset demonstrates the effectiveness of EMDMS and its advantages over some existing typical solutions.

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