Abstract: Federated learning is susceptible to backdoor attacks based on model replacement.In response to the poor performance of current backdoor detection methods,multi-party co-governance prevention strategy is proposed.The aim is to establish a co-go-vernance mechanism between the federated learning center server and the client,so as to effectively detect and prevent backdoors in the model without compromising data privacy and main task performance.This strategy covers shallow backdoor scanning,deep backdoor detection,and model repair,all of which are completed by the client in collaboration with the central server.Among them,shallow backdoor scanning is a lightweight real-time backdoor detection scheme that does not significantly increase time overhead.This scheme captures abnormal changes in the aggregated model parameters by the client and reports them to the central server.When the number of reports reaches the set threshold,the central server initiates deep backdoor detection,and each client pauses the federated learning process for deep detection to determine whether the neurons in the model are affected by backdoor attacks and exhibit abnormalities.If there are anomalies,each client adopts a method of concatenating a benign model and an attacked model to restore the model to a benign state,and submits the results of deep backdoor detection and model repair plans to the central server.It is up to the central server to decide the final repair plan,thereby thoroughly clearing the backdoor.Experimental results show that this strategy can effectively detect and remove backdoors in the federated learning model,ensuring the safe operation of horizontal federated learning.

Key words: Federated learning, Backdoor attack, Backdoor detection, Multi-party co-governance