Distributed Byzantine-Resilient Stochastic Optimization With Event-Triggered Communication
Shota Tanaka, Naoki Hayashi, Masahiro Inuiguchi · 2025
We consider Byzantine-resilient distributed optimization with event-triggered communication. The proposed algorithm is designed to handle non-convex optimization problems in a network of agents where some agents may exhibit Byzantine behavior. Each normal agent has an estimate of a critical point of the global cost function and transmits the estimate to neighbors when the difference between the current and previously communicated values exceeds a predefined threshold. Normal agents then update their estimates by averaging the received values from a trusted set that is determined through the Iterative Outlier Scissor (IOS) procedure. By combining the event-triggered communication and the IOS filtering procedure, the proposed approach ensures resilience to Byzantine behavior and guarantees convergence even in adversarial settings.