FEDERATED UNLEARNING METHOD BASED ON MALICIOUS TERMINAL INTERVENTION TRAINING

Abstract: a federated unlearning method based on malicious terminal intervention training and belongs to the technical field of privacy computing and federated learning, which eliminates the influence of the malicious client on the global model through federated unlearning and subtracts the parameter updates of the malicious client from the parameters of the final global model generated by federated learning to save the retraining time by continuing training with a theoretically unusable low-quality model. a comparison mechanism for judging the effect of the previous round of unlearning model and the effect of the current round of unlearning model to analyze the unlearning effects is also provided. the final unlearning model is trained with a small dataset and the deviations produced by the training process on the model are recovered, which effectively improves the accuracy of the final unlearning model.

Inventor(s): Dongsheng ZHOU, Xintong GUO, Pengfei WANG, Qiang ZHANG, Xiaopeng WEI, Ruiyun YU

CPC Classification: G06N3/098 (Distributed learning, e.g. federated learning)

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