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Iman Tabrizian

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TMLR Journal 2022 Journal Article

MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks

  • Ali Ramezani-Kebrya
  • Iman Tabrizian
  • Fartash Faghri
  • Petar Popovski

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.

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NeurIPS Conference 2020 Conference Paper

Adaptive Gradient Quantization for Data-Parallel SGD

  • Fartash Faghri
  • Iman Tabrizian
  • Ilia Markov
  • Dan Alistarh
  • Daniel M. Roy
  • Ali Ramezani-Kebrya

Many communication-efficient variants of SGD use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ. In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution. We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups. Our adaptive methods are also significantly more robust to the choice of hyperparameters.

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