.png){kind=icon}
返回
Backdoor attacks against distributed swarm learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.isatra.2023.03.034.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traditional machine learning approaches often need a central server, where raw datasets or model updates are trained or aggregated in a centralized way. However, these approaches are vulnerable to many attacks, especially by the malicious server. Recently, a new distributed machine learning paradigm, called Swarm Learning (SL), has been proposed to support no-central-server based decentralized training. In each training round, each participant node has a chance to be selected to serve as a temporary server. Thus, these participant nodes do not need to share their private datasets to ensure a fair and secure model aggregation in a central server. To the best of our knowledge, there are no existing solutions about the security threats in swarm learning. In this paper, we investigate how to implant backdoor attacks against swarm learning to illustrate its potential security risk. Experiment results confirm the effectiveness of our method with high attack accuracies in different scenarios. We also study several defense methods to alleviate these backdoor attacks. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.
Keyword:
Swarm learning
Backdoor attack
Distributed learning
Defense scheme
期刊
IF:
6.5
论文数:
5.9K
被引数:
2.0W
机构
引用论文
Ionic liquids derived from trialkylsulfonium bromides: Physicochemical properties and potential applications溴化三烷基锍衍生的离子液体: 物理化学性质和潜在应用
PREAI
分享收藏
分享收藏Deep Learning Based Attack Detection for Cyber-Physical System Cybersecurity: A Survey基于深度学习的网络物理系统网络安全攻击检测综述
PREAI
分享收藏Cyber security risk assessment for SCADA and DCS networksSCADA和DCS网络的网络安全风险评估.png)
ISA TRANSACTIONS
ISA TRANSACTIONSIF6.5
PREAI
分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏
查看更多内容