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[2602.19116] Event-Triggered Gossip for Distributed Learning
Summary
The paper presents an event-triggered gossip framework for distributed learning, enhancing communication efficiency among nodes while maintaining performance.
Why It Matters
This research addresses the critical challenge of communication bottlenecks in distributed learning systems. By reducing inter-node communication overhead, the proposed framework can significantly improve the scalability and efficiency of machine learning applications in decentralized environments, which is increasingly relevant in today's data-driven world.
Key Takeaways
- Introduces an adaptive communication control mechanism for decentralized learning.
- Achieves a 71.61% reduction in communication overhead compared to traditional methods.
- Maintains performance with only marginal losses despite reduced communication.
- Analyzes ergodic convergence under non-convex objectives.
- Provides insights into the conditions for effective model information exchange.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.19116 (eess) [Submitted on 22 Feb 2026] Title:Event-Triggered Gossip for Distributed Learning Authors:Zhiyuan Zhai, Xiaojun Yuan, Wei Ni, Xin Wang, Rui Zhang, Geoffrey Ye Li View a PDF of the paper titled Event-Triggered Gossip for Distributed Learning, by Zhiyuan Zhai and 5 other authors View PDF HTML (experimental) 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. Subjects: Signal Processing (eess.SP); Machine Learning (cs.LG) Cite as:...