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的通信方面，涉及多个智能体基于分布式数据，通过去中心化随机梯度下降（D-SGD）共同训练共享机器学习模型。具体而言，我们研究了广播传输与概率随机接入策略对D-SGD收敛性能的影响，同时考虑了无线信道的广播特性及通信拓扑中的链路动态性。研究结果表明，通过优化接入概率以最大化期望成功链路数，是加速系统收敛的高效策略。