A Blockchain Framework for Federated Learning: Privacy, Security, and Scalability
Abstract
Federated Learning (FL) has become the most popular solution for distributed AI to train models without transferring data to the central server. However, FL faces severe issues such as the risk of malicious updates, the absence of confidence in the central aggregation and security issues. This paper proposes a Blockchain-Enhanced Federated Learning (BEFL) framework that proposes the use of blockchain as a solution to these problems. Due to its distributed and irreversible nature that provides data consistency, open audit trails, and safe identification, Blockchain is an ideal partnership to FL. The authors then apply the BEFL on benchmark datasets and share some experimental results which show that BEFL is effective. In the MNIST dataset, overall BEFL acquires 98.1 % compared to FL having 95.5% and like centralized learning having 99.0 % accuracy. Likewise, BEFL yielded 92. 3% accuracy on CIFAR-10 and 87. 6% on the Healthcare data outperforming traditional FL on the same datasets with 89. 4% and 84. 1% accuracies respectively. The analysis of the required communication overhead showed that BEFL causes a small increase: 290 MB for CIFAR-10 compared to 250 MB for purely FL. Still, with the help of BEFL we gained faster convergence of the algorithm which needed 100 rounds on CIFAR-10 compared to 140 rounds needed by the traditional FL. Security analysis also revealed BEFL to be immune to both poisoning and Sybil attacks and will maintain optimal output even in an antagonistic…
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