ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning
This addresses the critical issue of fault tolerance in distributed learning systems, providing a practical solution for scenarios where data cannot be centralized and failures are common.
The paper tackles the problem of Byzantine failures in distributed machine learning by developing ByRDiE, a Byzantine-resilient distributed coordinate descent algorithm, which enables efficient high-dimensional learning in decentralized settings with theoretical and experimental validation.
Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfunctional occur frequently in the real world. This paper focuses on the problem of Byzantine failures, which are the hardest to safeguard against in distributed algorithms. While Byzantine fault tolerance has a rich history, existing work does not translate into efficient and practical algorithms for high-dimensional learning in fully distributed (also known as decentralized) settings. In this paper, an algorithm termed Byzantine-resilient distributed coordinate descent (ByRDiE) is developed and analyzed that enables distributed learning in the presence of Byzantine failures. Theoretical analysis (convex settings) and numerical experiments (convex and nonconvex settings) highlight its usefulness for high-dimensional distributed learning in the presence of Byzantine failures.