MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks

Published: 01 Oct 2022, Last Modified: 28 Feb 2023Accepted by TMLREveryoneRevisionsBibTeX
Abstract: Implementations of SGD on distributed and multi-GPU systems creates new vulnerabilities, which can be identified and misused by one or more adversarial agents. Recently, it has been shown that well-known Byzantine-resilient gradient aggregation schemes are indeed vulnerable to informed attackers that can tailor the attacks (Fang et al., 2020; Xie et al., 2020b). We introduce MixTailor, a scheme based on randomization of the aggregation strategies that makes it impossible for the attacker to be fully informed. Deterministic schemes can be integrated into MixTailor on the fly without introducing any additional hyperparameters. Randomization decreases the capability of a powerful adversary to tailor its attacks, while the resulting randomized aggregation scheme is still competitive in terms of performance. For both iid and non-iid settings, we establish almost sure convergence guarantees that are both stronger and more general than those available in the literature. Our empirical studies across various datasets, attacks, and settings, validate our hypothesis and show that MixTailor successfully defends when well-known Byzantine-tolerant schemes fail.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: - We made sure all reviews are properly addressed for the camera ready version. - A link to the code associated with our work is provided. - The deanonymized camera ready version is submitted following the decision email.
Code: https://github.com/Tabrizian/mix-tailor
Assigned Action Editor: ~Gautam_Kamath1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 271