Bant: Byzantine Antidote via Trial Function and Trust Scores

Gleb Molodtsov; Daniil Medyakov; Sergey Skorik; Nikolas Khachaturov; Shahane Tigranyan; Vladimir Aletov; Aram Avetisyan; Martin Takáč; Aleksandr Beznosikov

arXiv · 2505.07614

Bant: Byzantine Antidote via Trial Function and Trust Scores

Gleb Molodtsov·Daniil Medyakov·Sergey Skorik·Nikolas Khachaturov·Shahane Tigranyan·Vladimir Aletov·Aram Avetisyan·Martin Takáč·Aleksandr Beznosikov

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Abstract

Recent advancements in machine learning have improved performance while also increasing computational demands. While federated and distributed setups address these issues, their structures remain vulnerable to malicious influences. In this paper, we address a specific threat: Byzantine attacks, wherein compromised clients inject adversarial updates to derail global convergence. We combine the concept of trust scores with trial function methodology to dynamically filter outliers. Our methods address the critical limitations of previous approaches, allowing operation even when Byzantine nodes are in the majority. Moreover, our algorithms adapt to widely used scaled methods such as Adam and RMSProp, as well as practical scenarios, including local training and partial participation. We validate the robustness of our methods by conducting extensive experiments on both public datasets and private ECG data collected from medical institutions. Furthermore, we provide a broad theoretical analysis of our algorithms and their extensions to the aforementioned practical setups. The convergence guaranties of our methods are comparable to those of classical algorithms developed without Byzantine interference.

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Gleb Molodtsov, Daniil Medyakov, Sergey Skorik, Nikolas Khachaturov, Shahane Tigranyan, Vladimir Aletov, Aram Avetisyan, Martin Takáč, Aleksandr Beznosikov. 2025-12-04. Bant: Byzantine Antidote via Trial Function and Trust Scores. https://arxiv.org/abs/2505.07614

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