Abstract: Federated learning is a methodology that solves the contradiction of big data between user privacy and data sharing,and realize the concept of “data is invisible but available”.However,the federated model is at risk of backdoor attacks in the training process.The attacker trains a attack model containing a backdoor task locally,and amplifies the model parameters by a certain proportion to implant the backdoor into the federated model.Facing the backdoor threat in the training process of the horizontal federated learning,from the perspective of the game theory,this paper proposes a backdoor defense strategy and technical proposal based on the combination of random cutting and gradient clipping.After receiving the gradient from the participants,the central server randomly chooses the neural network layer from each participant,and aggregates the gradient contributions of each participant layer by layer.Then,the central sever clips gradient parameters according to gradient threshold.Gradient clipping and random cutting can weaken the influence generated by abnormal data from minority participants.It falls into platform state when the federated model learning backdoor features,so that it keeps failing on learning backdoor features without affecting the lear-ning process of target tasks.If the central server completes the federated learning during platform state,it can defend against backdoor attacks.Experimental results show that the proposed method can effectively defend against potential backdoor threats in fe-derated learning.At the same time,the accuracy of the model is ensured.Therefore,it can be applied in horizontal federated lear-ning scenarios,providing security protection for federated learning.

Key words: Horizontal federated learning, Backdoor attack, Random cutting, Gradient clipping