Linear Scalarization for Byzantine-Robust Learning on non-IID data

Latifa Errami, El houcine Bergou

Keywords: Byzantine SGD, Distributed Deep Learning, Non-IID

TL;DR: An enhancing method for current Byzantine defenses when data between clients is unbalanced.

Abstract:

In this work we study the problem of Byzantine-robust learning when data among clients is heterogeneous. We focus on poisoning attacks targeting the convergence of SGD. Although this problem has received great attention; the main Byzantine defenses rely on the IID assumption causing them to fail when data distribution is non-IID even with no attack.
We propose the use of Linear Scalarization (LS) as an enhancing method to enable current defenses to circumvent Byzantine attacks in the non-IID setting. The LS method is based on the incorporation of a trade-off vector that penalizes the suspected malicious clients.
Empirical analysis corroborates that the proposed LS variants are viable in the IID setting. For mild to strong non-IID data splits, LS is either comparable or outperforming current approaches under state-of-the-art Byzantine attack scenarios.

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