Event-Triggered Gossip for Distributed Learning

2026-02-22

AI Summary

This work addresses the high communication overhead in distributed learning by proposing an event-triggered decentralized gossip framework that enables nodes to adaptively determine communication times based on local model deviation, without requiring central coordination. By integrating event-triggered control with gossip protocols, the method ensures ergodic convergence under non-convex objectives while substantially reducing communication frequency. Experimental results demonstrate a 71.61% reduction in cumulative peer-to-peer transmission volume compared to a full-communication baseline, with only a negligible degradation in model performance.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

While distributed learning offers a new learning paradigm for distributed network with no central coordination, it is constrained by communication bottleneck between nodes. We develop a new event-triggered gossip framework for distributed learning to reduce inter-node communication overhead. The framework introduces an adaptive communication control mechanism that enables each node to autonomously decide in a fully decentralized fashion when to exchange model information with its neighbors based on local model deviations. We analyze the ergodic convergence of the proposed framework under noconvex objectives and interpret the convergence guarantees under different triggering conditions. Simulation results show that the proposed framework achieves substantially lower communication overhead than the state-of-the-art distributed learning methods, reducing cumulative point-to-point transmissions by \textbf{71.61\%} with only a marginal performance loss, compared with the conventional full-communication baseline.

Problem

• distributed learning
• communication bottleneck
• event-triggered
• gossip
• decentralized

Innovation

• event-triggered gossip
• distributed learning
• communication efficiency
• adaptive communication control
• decentralized optimization