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Mitigate noisy data for smart IoT via GAN based machine unlearning

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With the development of IoT applications,machine learning dramatically improves the utility of variable IoT systems such as autonomous driving.Although the pretrain-finetune framework can cope well with data heterogeneity in complex IoT scenarios,the data collected by sensors often contain unexpected noisy data,e.g.,out-of-distribution(OOD)data,which leads to the reduced performance of fine-tuned models.To resolve the problem,this paper proposes MuGAN,a method that can mitigate the side-effect of OOD data via the generative adversarial network(GAN)-based machine unlearning.MuGAN follows a straightforward but effective idea to mitigate the performance loss caused by OOD data,i.e.,"flashbacking"the model to the condition where OOD data are excluded from model training.To achieve the goal,we design an adversarial game,where a discriminator is trained to identify whether a sample belongs to the training set by observing the confidence score.Meanwhile,a generator(i.e.,the target model)is updated to fool the discriminator into believing that the OOD data are not included in the training set but others do.The experimental results show that benefiting from the high unlearning rate(more than 90％)and retention rate(99％),MuGAN succeeds in lowering the model performance degradation caused by OOD data from 5.88％to 0.8％.

machine unlearninggenerative adversarial networkout of distribution dataInternet of Thingneural network

Zhuo MA、Yilong YANG、Yang LIU、Xinjing LIU、Jianfeng MA

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School of Cyber Engineering,Xidian University,Xi'an 710071,China

State Key Laboratory of Integrated Services Networks(ISN),Xi'an 710071,China

国家重点研发计划国家自然科学基金国家自然科学基金Natural Science Basic Research Program of Shaanxi Province陕西省重点研发计划高等学校学科创新引智计划(111计划)

2022YFB3103500U21A20464618722832021JC-222022GY-029B16037

2024

10.1007/s11432-022-3671-9

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(3)
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