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

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

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...

詳細記述

書誌詳細
主要な著者: He, Weiyang, Liu, Zizhen, Chang, Chip Hong
その他の著者: School of Electrical and Electronic Engineering
フォーマット: Conference Paper
言語:English
出版事項: 2024
主題:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Federated Learning
Knowledge Distillation
Backdoor Attacks
オンライン･アクセス:https://hdl.handle.net/10356/173117

インターネット

https://hdl.handle.net/10356/173117

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