BADFL: Backdoor attack defense in federated learning from local model perspective

• Main Authors: ZHANG, Haiyan, LI, Xinghua, XU, Mengfan, LIU, Ximeng, WU, Tong, WENG, Jian, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Servers; Artificial Neural Networks; Accuracy; Training; Fires; Anomaly Detection; Adaptation Models; Federated Learning; Backdoor Attack; Clustering; Interpretability; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9535; https://ink.library.smu.edu.sg/context/sis_research/article/10535/viewcontent/BADFL_av.pdf
• Institution: Singapore Management University

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model,...