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Improving Federated Learning through abnormal Client Detection and Incentive

Computer Modeling in Engineering & Sciences 2024年第4期139卷 383-403页

作者： Hongle Guo Yingchi Mao Xiaoming He Benteng Zhang Tianfu Pang Ping Ping College of Computer and Information Hohai UniversityNanjing211100China Key Laboratory ofWater Big Data Technology of Ministry ofWater Resources Hohai UniversityNanjing211100China

Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central *** reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their *** central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective *** reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize *** this paper,we propose a Federated Learning framework for Detecting and Incentivizing abnormal clients(FL-DIAC)to accomplish efficient and security federated *** build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection *** them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational ***,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively *** training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in *** to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.

关键词： Federated learning abnormal clients incentive credit score abnormal score detection

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