Byzantine-Robust Gossip: Insights from a Dual Approach
This work addresses security vulnerabilities in decentralized learning systems, which is an incremental improvement in a domain-specific area.
The paper tackles the problem of Byzantine attacks in decentralized distributed learning by proposing a Byzantine-robust algorithm using a dual approach, providing convergence guarantees for average consensus and experimental validation.
Distributed learning has many computational benefits but is vulnerable to attacks from a subset of devices transmitting incorrect information. This paper investigates Byzantine-resilient algorithms in a decentralized setting, where devices communicate directly in a peer-to-peer manner within a communication network. We leverage the so-called dual approach for decentralized optimization and propose a Byzantine-robust algorithm. We provide convergence guarantees in the average consensus subcase, discuss the potential of the dual approach beyond this subcase, and re-interpret existing algorithms using the dual framework. Lastly, we experimentally show the soundness of our method.