Federated Learning Attack Defense Mechanism Based on Multi-weighted Subjective Logic
Federated learning,as a distributed learning framework,can jointly participate in training a global model while ensuring the local data security of each client.However,in the federation learning process,there exist malicious participants who submit wrong updates to pre-vent the convergence of the model or make the model fit deviate from the normal direction by poisoning attacks.The traditional subjective logic defense mechanism considers the frequency of interaction,the time of interaction and the influence on each other,ignoring the influence of multiple sources of data on the reputation evaluation results.To address this problem,this paper proposes a federal learning attack defense mechanism based on multi-weighted subjective logic.The mechanism calculates the client's contribution by Shapley value and evaluates the client's reputation in federation learning in three aspects:trustworthiness,contribution and freshness.Meanwhile,the security of the model is further improved by introducing blockchain technology to store the parameters.The experimental results show that the algorithm in this paper can accurately identify and defend against poisoning attacks under multi-source data,while retaining high model performance.
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