Federated learning algorithm based on matrix mapping for data privacy over edge computing

Abstract

Purpose This paper aims to provide the security and privacy for Byzantine clients from different types of attacks. Design/methodology/approach In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing. Findings By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped. Originality/value By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.