Federated learning (FL) is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. One central server is not enough, due to problems of connectivity with clients. In this paper, a decentralized federated learning (DFL) model with the stochastic gradient descent (SGD) algorithm has been introduced, as a more scalable approach to improve the learning performance in a network of agents with arbitrary topology. Three scheduling policies for DFL have been proposed for communications between the clients and the parallel servers, and the convergence, accuracy, and loss have been tested in a totally decentralized mplementation of SGD. The experimental results show that the proposed scheduling polices have an impact both on the speed of convergence and in the final global model.

翻译：联邦学习（FL）是一种分布式机器学习范式，其中大量客户端与中央服务器协调，在不共享自身训练数据的情况下学习模型。由于客户端与服务器的连通性问题，单一中央服务器不足以应对。本文引入了一种基于随机梯度下降（SGD）算法的去中心化联邦学习（DFL）模型，作为一种更具扩展性的方法，用于提升任意拓扑结构代理网络中的学习性能。针对客户端与并行服务器之间的通信，提出了三种DFL调度策略，并在完全去中心化的SGD实现中测试了收敛性、准确率和损失。实验结果表明，所提出的调度策略对收敛速度及最终全局模型均有显著影响。