Persistent Backdoor Attack for Federated Learning Based on Trigger Differential Optimization

The distributed nature of federated learning allows each client to train the model while maintaining data independence,but this also allows attackers to control or mimic some clients to launch backdoor attacks by implanting carefully designed fixed triggers to manipulate the model output.The effectiveness and persistence of triggers are important criteria for measuring attack effectiveness.Effectiveness pertains to the rate of successful breaches,while persistence embodies the capability to sustain a high success rate even after the cessation of the attack.At present,research on effectiveness has been relatively in-depth,but maintaining the persistence of triggers remains a challenging issue.A backdoor attack method based on dynamic optimization triggers is proposed to extend the persistence of triggers.Firstly,during dynamic updates in federated learning,triggers are synchronously optimized to minimize the difference between the potential representations of trigger features during and after attacks,thereby training the global model's ability to remember trigger features.Secondly,using redundant neurons as an indicator of the success of implanting backdoors to adaptively add noise and enhance the effectiveness of attacks.Extensive experiments on the MNIST,CIFAR-10,and CIFAR-100 datasets have shown that the proposed scheme effectively extends the persistence of triggers in fede-rated learning environments.Under five kind of representative defense systems,the success rate of attacks is higher than 98%,especially after more than 600 rounds of attacks on the CIFAR-10,the success rate of attacks still exceeds 90%.