Yongsheng Zhu1,2,*, Chong Liu3,4, Chunlei Chen5, Xiaoting Lyu3,4, Zheng Chen3,4, Bin Wang6, Fuqiang Hu3,4, Hanxi Li3,4, Jiao Dai3,4, Baigen Cai1, Wei Wang3,4
CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1305-1325, 2024, DOI:10.32604/cmes.2024.054820
Abstract The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency. Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data. However, despite its privacy benefits, federated learning systems are vulnerable to poisoning attacks, where adversaries alter local model parameters on compromised clients and send malicious updates to the server, potentially compromising the global model’s accuracy. In this study, we introduce PMM (Perturbation coefficient Multiplied by Maximum value), a new poisoning attack method that perturbs model More >
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