论文标题
在不可靠的无线网络上的异步分散学习
Asynchronous Decentralized Learning over Unreliable Wireless Networks
论文作者
论文摘要
分散的学习使Edge用户可以通过设备到设备通信交换信息来协作训练模型，但是先前的工作仅限于具有固定拓扑和可靠工人的无线网络。在这项工作中，我们提出了一种异步分散的随机梯度下降（DSGD）算法，该算法对于在无线网络边缘出现的固有计算和通信失败非常可靠。我们从理论上分析其性能并建立非质合收敛保证。实验结果证实了我们的分析，证明了异步性和过时的梯度信息在分散学习中与不可靠的无线网络的重复使用。
Decentralized learning enables edge users to collaboratively train models by exchanging information via device-to-device communication, yet prior works have been limited to wireless networks with fixed topologies and reliable workers. In this work, we propose an asynchronous decentralized stochastic gradient descent (DSGD) algorithm, which is robust to the inherent computation and communication failures occurring at the wireless network edge. We theoretically analyze its performance and establish a non-asymptotic convergence guarantee. Experimental results corroborate our analysis, demonstrating the benefits of asynchronicity and outdated gradient information reuse in decentralized learning over unreliable wireless networks.
