An empirical study of the inherent resistance of knowledge distillation based federated learning to targeted poisoning attacks

• Main Authors: He, Weiyang, Liu, Zizhen, Chang, Chip Hong
• Other Authors: School of Electrical and Electronic Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2024
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Federated Learning; Knowledge Distillation; Backdoor Attacks
• Online Access: https://hdl.handle.net/10356/173117
• Institution: Nanyang Technological University

While the integration of Knowledge Distillation (KD) into Federated Learning (FL) has recently emerged as a promising solution to address the challenges of heterogeneity and communication efficiency, little is known about the security of these schemes against poisoning attacks prevalent in vanilla F...