Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization

• URL: http://arxiv.org/abs/2304.14024v1
• Date: Thu, 27 Apr 2023 08:41:57 GMT
• Title: Attacks on Robust Distributed Learning Schemes via Sensitivity Curve Maximization
• Authors: Christian A. Schroth and Stefan Vlaski and Abdelhak M. Zoubir
• Abstract summary: We present a new attack based on sensitivity of curve (SCM) We demonstrate that it is able to disrupt existing robust aggregation schemes by injecting small but effective perturbations.
• Score: 37.464005524259356
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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