Bant: Byzantine Antidote via Trial Function and Trust Scores
Molodtsov, Gleb Medyakov, Daniil Skorik, Sergey Khachaturov, Nikolas Tigranyan, Shahane Aletov, Vladimir Avetisyan, Aram Takac, Martin Beznosikov, Aleksandr
Molodtsov, Gleb
Medyakov, Daniil
Skorik, Sergey
Khachaturov, Nikolas
Tigranyan, Shahane
Aletov, Vladimir
Avetisyan, Aram
Takac, Martin
Beznosikov, Aleksandr
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
License
Language
Collections
Research Projects
Organizational Units
Journal Issue
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.
Citation
G. Molodtsov, D. Medyakov, S. Skorik, N. Khachaturov, S. Tigranyan, V. Aletov , et al., "Bant: Byzantine Antidote via Trial Function and Trust Scores," 2026, pp. 24431-24440.
Source
Proceedings of the AAAI Conference on Artificial Intelligence
Conference
The Fortieth AAAI Conference on Artificial Intelligence
Keywords
46 Information and Computing Sciences, 4611 Machine Learning
Subjects
Source
The Fortieth AAAI Conference on Artificial Intelligence
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)