Impact of Network Topology on the Convergence of Decentralized Federated Learning Systems

Hanna Kavalionak, E. Carlini, Patrizio Dazzi, L. Ferrucci, M. Mordacchini, M. Coppola
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引用次数: 4

Abstract

Federated learning is a popular framework that enables harvesting edge resources' computational power to train a machine learning model distributively. However, it is not always feasible or profitable to have a centralized server that controls and synchronizes the training process. In this paper, we consider the problem of training a machine learning model over a network of nodes in a fully decentralized fashion. In particular, we look for empirical evidence on how sensitive is the training process for various network characteristics and communication parameters. We present the outcome of several simulations conducted with different network topologies, datasets, and machine learning models.
网络拓扑对分散联邦学习系统收敛性的影响
联邦学习是一种流行的框架,它可以利用边缘资源的计算能力来分布式地训练机器学习模型。然而,拥有一个控制和同步培训过程的集中式服务器并不总是可行或有利可图的。在本文中,我们考虑以完全分散的方式在节点网络上训练机器学习模型的问题。特别是,我们寻找关于各种网络特征和通信参数的训练过程有多敏感的经验证据。我们介绍了使用不同网络拓扑、数据集和机器学习模型进行的几种模拟的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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