FedBC: Blockchain-based Decentralized Federated Learning

Wu，Xin1; Wang，Zhi1; Zhao，Jian2; Zhang，Yan3; Wu，Yu3

10.1109/ICAICA50127.2020.9182705

2020-06-01

978-1-7281-7006-0

Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

217-221

27-29 June 2020

Dalian, China

Federated learning enables participants to collaborate on model training without directly exchanging raw data. Existing federated learning methods often follow the parameter server architecture, using third-party collaborators to provide aggregation and key management. In this case, the central node obtains information uploaded by other nodes. Studies have shown that with this information, the central node can infer important information, which leads to data privacy leakage. In addition, the failure on the server node can also cause the entire system to fail. We designed a completely decentralized federated learning framework based on blockchain, thereby avoiding the privacy and failure risk of the centralized structure. Moreover, we develop the corresponding model training approach. Compared with the existing methods, our framework performs better in terms of accuracy, robustness, and privacy.

blockchain deep learning distributed computing federated learning

其他

英语

EI

20204109327732

Deep learning ; Data privacy ; Learning systems

Ergonomics and Human Factors Engineering:461.4 ; Database Systems:723.3

2-s2.0-85092187252

Scopus

1.Tsinghua University,Beijing,China
2.Shenzhen Technology University,Shenzhen,China
3.Southern University of Science and Technology,Peng Cheng Laboratory,Shenzhen,China

Wu，Xin,Wang，Zhi,Zhao，Jian,et al. FedBC: Blockchain-based Decentralized Federated Learning[C],2020:217-221.