Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

Mochan Fan, Kailai Ji, Zhaofeng Zhang, Hongfang Yu, Gang Sun

Abstract: The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability, and privacy protection of blockchain federated learning (BFL), some BFL schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this article, we propose a new decentralized, secure and verifiable consortium BFL privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and data set of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make BFL more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.