Computer Science > Machine Learning
[Submitted on 27 Apr 2023]
Title:Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
View PDFAbstract:Distributed learning paradigms, such as federated or decentralized learning, allow a collection of agents to solve global learning and optimization problems through limited local interactions. Most such strategies rely on a mixture of local adaptation and aggregation steps, either among peers or at a central fusion center. Classically, aggregation in distributed learning is based on averaging, which is statistically efficient, but susceptible to attacks by even a small number of malicious agents. This observation has motivated a number of recent works, which develop robust aggregation schemes by employing robust variations of the mean. We present a new attack based on sensitivity curve maximization (SCM), and demonstrate that it is able to disrupt existing robust aggregation schemes by injecting small, but effective perturbations.
Submission history
From: Christian Alexander Schroth [view email][v1] Thu, 27 Apr 2023 08:41:57 UTC (2,539 KB)
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