Research Article
Effective Blockchain-Based Asynchronous Federated Learning for Edge-Computing
@INPROCEEDINGS{10.1007/978-3-031-24383-7_28, author={Zhipeng Gao and Huangqi Li and Yijing Lin and Ze Chai and Yang Yang and Lanlan Rui}, title={Effective Blockchain-Based Asynchronous Federated Learning for Edge-Computing}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2023}, month={1}, keywords={Federated learning Blockchain Edge-computing Asynchronous architecture Decentralization}, doi={10.1007/978-3-031-24383-7_28} }- Zhipeng Gao
Huangqi Li
Yijing Lin
Ze Chai
Yang Yang
Lanlan Rui
Year: 2023
Effective Blockchain-Based Asynchronous Federated Learning for Edge-Computing
COLLABORATECOM
Springer
DOI: 10.1007/978-3-031-24383-7_28
Abstract
Since massive data are generated at the network’s edge, the Internet of Things devices can exploit edge computing and federated learning to train artificial intelligence (AI) models while protecting data privacy. However, heterogeneous devices lead to low efficiency and single-point-of-failure. Moreover, malicious nodes may affect training accuracy. Therefore, we propose FedLyra, an effective blockchain-based asynchronous federated learning architecture, to improve the efficiency of aggregation and resist malicious nodes in a trusted and decentralized manner. We then propose a reputation mechanism that combines historical behaviors and the quality of local updates to resist disagreements and adversaries. With the help of the reputation mechanism, we propose a council-based decentralized aggregation mechanism to exclude malicious nodes. Experiments show that FedLyra can resist malicious nodes and ensure the accuracy of training results.
