Two-Layered Blockchain Architecture for Federated Learning over Mobile Edge Network

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.

Automated summary

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.