分布式学习中模型中毒的攻击-模型不可知防御

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Hairuo Xu, Tao Shu
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引用次数: 0

摘要

分布式学习的分布式特性使得学习过程容易受到模型中毒攻击。现有的大多数对抗措施都是基于假定的攻击模型设计的,并且只能在假定的攻击模型下执行。然而,在现实中,分布式学习系统在部署学习系统时,通常无法知道它在运行中实际面临的攻击模型,因此构成了系统的零日漏洞,到目前为止,这在很大程度上被忽视了。在本文中,我们研究了分布式学习的攻击模型无关防御机制,该机制能够在不依赖于特定攻击模型假设的情况下对抗广泛的模型中毒攻击,从而减轻系统的零日漏洞。进行了大量的实验来验证所提出的防御的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Attack-Model-Agnostic Defense Against Model Poisonings in Distributed Learning
The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense.
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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