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.

中文翻译：

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边缘AI计算领域协作学习的可信共识方案


使用多个边缘设备进行协作学习以构建群体智能是一种新趋势。边缘人工智能（AI）计算往往充分利用终端设备、边缘服务器和云数据中心中的各种可用数据和资源来实现协同决策。但要实现这一目标，还应保证数据存储、传输、管理的安全。因此，如何构建边缘计算中协作学习的分布式信任系统是一个复杂且具有挑战性的工程问题。区块链可以帮助实现分布式协作学习，无需第三方审核。此外，共识协议也发挥着关键技术作用。本文提出了一种边缘AI多方协同学习的可信共识方案。为了提高系统安全性，采用基于信誉的权限来支持异常节点的快速移除。采用声誉奖惩的方法，根据参与共识过程的节点的行为对其声誉进行评分并记录在链上。做出恶意行为的节点将受到降低声誉值的惩罚。节点根据声誉值进行评级，声誉值越高的节点被共识认可，拥有更多的信用和权利。由于信誉模块可在 X-BFT 协议上加载，因此在实际应用中很容易实施。实验结果表明，XR-BFT 攻击者被选为领导者的概率比 X-BFT 协议低 87.5%，从而使我们的协议能够更安全、更高效地实现边缘 AI 计算中的信任协作学习。