Federated edge learning model based on multi-level proxy permissioned blockchain - GE Li’na; LI Haiao; WANG Jie (Tongxin xuebao)
Aiming at the problems of privacy security and low learning efficiency faced by federated learning in zero trust edge computing environment, a federated learning model based on multi-level proxy permission blockchain for edge computing was proposed. The multi-level proxy permission blockchain was designed to establish a trusted underlying environment for federated edge learning, and the hierarchical model aggregation scheme was implemented to alleviate the pressure of model training. A hybrid strategy was devised to enhance model privacy using secret sharing and differential privacy. A federated task node selection algorithm based on reputation verification was devised to address the problem of zero or extremely poor credibility of edge clients. Positive training samples and the local model were utilized as reputation rewards to refine the security verification scheme, and further ensure the effectiveness of the model against malicious adversaries. Experimental results show that under the attack of 40% malicious adversaries, compared with the existing advanced schemes, the accuracy of the proposed scheme is improved by 10%, and high privacy security is achieved with high model accuracy.