Decentralized Federated Learning Framework with Blockchain for Verifiable and Trusted Collaboration

Decentralized Federated Learning Framework with Blockchain for Verifiable and Trusted Collaboration

연구 분야: Verification

학회: 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS)

Federated Learning (FL) is an innovative distributed machine learning approach that allows multiple participants to collaboratively train a global model without sharing their private data. While effective, traditional FL frameworks often rely on centralized servers for model aggregation, making them susceptible to single points of failure, security breaches, and trust concerns. To address these challenges, this paper introduces a Decentralized Federated Learning Framework (DFLF) integrated with blockchain technology. The proposed framework eliminates the need for a central coordinator by utilizing blockchain’s immutable ledger and decentralized architecture. This integration ensures secure model sharing, verifiable updates, and incentivized collaboration among participants. Smart contracts automate key operations, including model aggregation, contribution validation, and reward distribution, thereby enhancing efficiency and transparency. The framework was implemented using a permissioned blockchain and tested on a healthcare dataset for patient diagnosis prediction. Experimental results demonstrate improved model accuracy, reduced latency, and enhanced trust compared to conventional FL approaches. The inclusion of blockchain ensures robust privacy preservation and builds trust in collaborative machine learning. This paper provides a comprehensive analysis of the framework, including its design, algorithms, mathematical formulations, and performance metrics. The results highlight the potential of combining blockchain with FL to establish a scalable, secure, and trustworthy ecosystem for decentralized machine learning applications.