通过自适应触发机制降低分布式学习通信开销
Event-Triggered Gossip for Distributed Learning
- 节点根据本地模型偏差自主决定通信时机，完全去中心化
- 相比基线方法通信量减少71.61%，性能损失微小
- 适用于资源受限的分布式系统，如边缘计算场景
分布式学习在无中心协调的网络中提供新范式，但受限于节点间通信瓶颈。本文提出一种新型事件触发式扩散框架，以降低节点间通信开销。该框架引入自适应通信控制机制，使每个节点能基于本地模型偏差，在完全去中心化方式下自主决定何时与邻居交换模型信息。我们在非凸目标下分析了该框架的遍历收敛性，并解释了不同触发条件下的收敛保证。仿真结果表明，所提框架相比现有先进方法显著降低通信开销，在仅带来微小性能损失的前提下，累计点对点传输次数减少了71.61%。
原文摘要 · Abstract (English)
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.
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