Blockchain-Enabled Federated Learning: A Hyperledger Fabric Approach for Secure IoT Systems

The rapid growth of loT devices necessitates robust solutions to ensure data privacy and security in distributed learning environments. This paper proposes a novel frame-work combining federated learning with Hyperledger Fabric blockchain to enable decentralized model training while safe-guarding sensitive data. By leveraging blockchain's immutability and auditability alongside federated learning's data localization, the system establishes a secure and transparent ecosystem for collaborative machine learning. The paper details the system architecture, implementation strategies, and integration chal-lenges, offering a practical solution for secure, privacy-preserving learning in loT environments and highlighting the potential of blockchain-enabled federated learning for decentralized, data-sensitive applications.