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Event-Triggered Gossip for Distributed Learning
Summary
This study introduces an event-triggered gossip framework for distributed learning, significantly reducing communication overhead. The novel approach cuts transmissions by over 70% with minimal performance impact.
Area of Science:
- Computer Science
- Machine Learning
- Distributed Systems
Background:
- Distributed learning enables decentralized network training but faces communication bottlenecks.
- Existing methods often incur high inter-node communication costs.
Purpose of the Study:
- To develop an event-triggered gossip framework to minimize communication overhead in distributed learning.
- To enable autonomous, decentralized communication control based on local model deviations.
Main Methods:
- Proposed an adaptive communication control mechanism for event-triggered gossip.
- Analyzed ergodic convergence under non-convex objectives.
- Interpreted convergence guarantees under various triggering conditions.
Main Results:
- The framework substantially reduces communication overhead compared to state-of-the-art methods.
- Achieved a 71.61% reduction in cumulative point-to-point transmissions.
- Demonstrated only marginal performance loss compared to a full-communication baseline.
Conclusions:
- The event-triggered gossip framework effectively mitigates communication bottlenecks in distributed learning.
- Offers a promising approach for efficient decentralized training with reduced overhead.
- Adaptive, decentralized control is key to improving communication efficiency.
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