DAGUARD: distributed backdoor attack defense scheme under federated learning

Abstract

In order to solve the problems of distributed backdoor attack under federated learning, a distributed backdoor attack defense scheme (DAGUARD) under federated learning was proposed based on the assumption that the server selected no more than half of malicious clients for global aggregation.The partial update strategy of the triple gradient optimization algorithm (TernGrad) was designed to solve the backdoor attack and inference attack, an adaptive density clustering defense scheme was designed to solve the backdoor attacks with relatively large angle deflection, the adaptive clipping scheme was designed to limit the enhancement backdoor attack that amplify the gradients and the adaptive noise-enhancing scheme was designed to weaken distributed backdoor attacks.The experimental results show that in the federated learning scenario, the proposed scheme has better defense performance and defense stability than existing defense strategies.

Keywords

• federated learning
• distributed backdoor attack
• cluster
• differential privacy