FedBN: A Communication-Efficient Federated Learning Strategy Based on Blockchain

Abstract

The integration of blockchain technology into the federated learning (FL) process offers effective measures for safeguarding the security and privacy of model data. However, the inherent consensus mechanism of blockchain technology can introduce long latency, which may hinder the overall efficiency of FL. To address this challenge, we propose a blockchain-based FL training strategy that tackles the following issues: (1) Reducing the number of parameter aggregations in the blockchain network by increasing the number of local training epochs, which effectively minimize the frequency of blockchain authentication and packing operations. (2) Mitigating communication overhead between terminals and edge nodes in the blockchain network by leveraging the wait-free backpropagation technique, which reduces the communication overhead. Experimental results demonstrate that our proposed strategy yields improvements in both convergence efficiency and system scalability.