A Trusted Consensus Scheme for Collaborative Learning in the Edge AI Computing Domain

Abstract

Collaborative learning with multiple edge devices to build group intelligence is a new trend. Edge artificial intelligence (AI) computing often makes full use of various available data and resources in terminal devices, edge servers, and cloud data centers to achieve collaborative deci-sion-making. However, in order to achieve the goal, it should also guarantee the security of data storage, transmission, and management. Therefore, how to build a distributed trust system for cooperative learning in edge computing is a complex and challenging engineering problem. Block-chain can help realize collaborative learning in a distributed way without the need for third-party audit. Also, the consensus protocol plays a key technology role. In this article, a trusted consensus scheme for multi-party collaborative learning of edge AI is proposed. To improve system security, reputation-based rights are used to support fast removal of abnormal nodes. With the method of reputation rewards and punishments, the reputations of the nodes participating in the consensus process are scored and recorded on the chain according to their behavior. Nodes who commit malicious actions will be punished by reducing their reputation values. Nodes are rated according to their reputation value, and those with higher reputation rates are acknowledged with more credits and rights in consensus. Since the reputation module is loadable on X-BFT protocols, it is easy to enforce in real applications. The experimental results show that the probability of the attacker being chosen as the leader of XR-BFT is 87.5 percent lower than that of the X-BFT protocols, thus enabling our protocol to achieve trust collaborative learning in edge AI computing with more safety and efficiency.