期刊
IEEE NETWORK
卷 36, 期 1, 页码 45-51出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MNET.011.2000339
关键词
blockchain; federated learning; security; trustful mobile edge network
类别
资金
- National Key R&D Program of China [2018YFE0205502]
The paper introduces a framework that combines blockchain and federated learning to address security and trust issues in FL on mobile edge networks. The framework includes a two-layered architecture with local and global model update chains, as well as a reputation-learning based incentive mechanism to make participant devices more trustworthy.
Federated learning (FL) is seen as a road towards privacy-preserving distributed artificial intelligence (AI) while keeping the raw training data on the local device. By leveraging blockchain, this paper puts forward a blockchain and FL fusioned framework to manage the security and trust issues when applying FL over mobile edge networks. First, a two-layered architecture is proposed that consists of two types of blockchains: local model update chain (LMUC) assisted by device-to-device (D2D) communication and global model update chain (GMUC) supporting task sharding. D2D-assisted LMUC is designed to chronologically and efficiently record all of the local model training results, which can help to form a long-term reputation of local devices. The GMUC is proposed to provide both security and efficiency by preventing mobile edge computing (MEC) nodes from malfunctioning and dividing it into logically-isolated FL task-specific chains. Then, a reputation-learning based incentive mechanism is introduced to make the participant local devices more trustful with the reward implemented by a smart contract. Finally, a case study is given to show that the proposed framework performs well in terms of FL learning accuracy and blockchain time delay.
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