BGFL: a blockchain-enabled group federated learning at wireless industrial edges

In the rapidly evolving landscape of Industry 4.0, the complex computational tasks and the associated massive data volumes present substantial opportunities for advancements in machine learning at industry edges. Federated learning (FL), which is a variant of distributed machine learning for edge-cloud computing, presents itself as a persuasive resolution for these industrial edges, with its main objectives being the mitigation of privacy breaches and the resolution of data privacy concerns. However, traditional FL methodologies encounter difficulties in effectively overseeing extensive undertakings in Industry 4.0 as a result of challenges including wireless communications with high latency, substantial heterogeneity, and insufficient security protocols. As a consequence of these obstacles, blockchain technology has garnered acclaim for its secure, decentralized, and transparent data storage functionalities. A novel blockchain-enabled group federated learning (BGFL) framework designed specifically for wireless industrial edges is presented in this paper. By strategically dividing industrial devices into multiple groups, the BGFL framework simultaneously reduces the wireless traffic loads required for convergence and improves the accuracy of collaborative learning. Moreover, to optimize aggregation procedures and reduce communication resource utilization, the BGFL employs a hierarchical aggregation strategy that consists of both local and global aggregation off-chain and on-chain, respectively. The integration of a smart contract mechanism serves to fortify the security framework. The results of comparative experimental analyses demonstrate that the BGFL framework enhances the resilience of the learning framework and effectively reduces wireless communication latency. Thus, it offers a scalable and efficient solution for offloading tasks in edge-cloud computing environments.