VB-FLChain: a voting-based blockchain-enabled federated learning platform

Yue Li; Anxing Wen; Xian Xie; Ang Li

doi:10.1117/12.2668256

20 April 2023 VB-FLChain: a voting-based blockchain-enabled federated learning platform

Yue Li, Anxing Wen, Xian Xie, Ang Li

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126022Q (2023) https://doi.org/10.1117/12.2668256
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

In recent years, demand for collaborative model training has increased significantly. The key to realize decentralized model training without data disclosure is Federated Learning(FL). However, current research has not yet implemented a concrete method taking into account both efficiency and fault tolerance. And the incentive has not been fully considered. In this paper, the article proposes a novel voting-based blockchain-enabled federated learning platform named as VB-FLChain. The platform can be made incentive-aware by reward distribution in blockchain. With FL combined voting methods, the platform is more secure besides the high efficiency. Finally, the performance of VB-FLChain is measured by applying the UNSW-NB15 dataset.

Citation Download Citation

Yue Li, Anxing Wen, Xian Xie, and Ang Li "VB-FLChain: a voting-based blockchain-enabled federated learning platform", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126022Q (20 April 2023); https://doi.org/10.1117/12.2668256

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