A Zero-Knowledge Proof Framework for Securing Federated Learning in Healthcare Using Blockchain Technology

Associate Professor, Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India
Assistant Professor, Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India
Assistant Professor, Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India
Professor, Department of Computer Science and Engineering Acharya Institute of Technology Bengaluru, India
Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India
Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India
Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India
Department of Information Science and Engineering Acharya Institute of Technology Bengaluru, India

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Abstract

The growing dependance on data-based decisionmaking in healthcare has brought attention to the vital importance of secure, privacy-preserving and collaborative learning techniques. Traditional centralized learning approaches in medical data often raise concerns regarding patient privacy data leaks and even regulatory troubles. Federated learning came as a good solution, where it allows model training in different hospitals without sharing the sensitive patient data. However, federated learning has its problems - it can be mislead with fake updates, the model can even be poisoned and it is really hard to trust every participants involved.

In this work, we present fed-chain, a secure and scalable framework which brings together federated learning, blockchain and zero-knowledge-proofs(ZKPs) preserving the privacy of patient's data in healthcare. Blockchain here adds decentralized trust, immutability and makes model updates transparent to review while ZKPs helps in proving correctness without leaking personal data. We are implemented this framework for heart disease prediction where multiple hospitals train the model together but the data stays confidential. Our experimental results shown better accuracy, more strength against attacks and even low communication cost compared to other FL setups.

Overall, the systems gives a safer approach for working together on healthcare data, allowing hospitals and research centers to generate valuable predictions using these models while keeping the patient data private and safe.