In this work, we focus on the communication aspect of decentralized learning, which involves multiple agents training a shared machine learning model using decentralized stochastic gradient descent (D-SGD) over distributed data. In particular, we investigate the impact of broadcast transmission and probabilistic random access policy on the convergence performance of D-SGD, considering the broadcast nature of wireless channels and the link dynamics in the communication topology. Our results demonstrate that optimizing the access probability to maximize the expected number of successful links is a highly effective strategy for accelerating the system convergence.

翻译：本文聚焦于去中心化学习中的通信问题，该场景涉及多个智能体利用分布式随机梯度下降算法在分布数据上协同训练共享机器学习模型。我们重点研究了无线信道的广播特性与通信拓扑中的链路动态对D-SGD收敛性能的影响，特别是广播传输和概率随机接入策略的作用。结果表明，通过优化接入概率以最大化成功链路的期望数量，是一种加速系统收敛的高效策略。