In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) has been the most used approach, where a central entity aggregates participants' models to create a global one. However, CFL presents limitations such as communication bottlenecks, single point of failure, and reliance on a central server. Decentralized Federated Learning (DFL) addresses these issues by enabling decentralized model aggregation and minimizing dependency on a central entity. Despite these advances, current platforms training DFL models struggle with key issues such as managing heterogeneous federation network topologies. To overcome these challenges, this paper presents Fedstellar, a novel platform designed to train FL models in a decentralized, semi-decentralized, and centralized fashion across diverse federations of physical or virtualized devices. The Fedstellar implementation encompasses a web application with an interactive graphical interface, a controller for deploying federations of nodes using physical or virtual devices, and a core deployed on each device which provides the logic needed to train, aggregate, and communicate in the network. The effectiveness of the platform has been demonstrated in two scenarios: a physical deployment involving single-board devices such as Raspberry Pis for detecting cyberattacks, and a virtualized deployment comparing various FL approaches in a controlled environment using MNIST and CIFAR-10 datasets. In both scenarios, Fedstellar demonstrated consistent performance and adaptability, achieving F1 scores of 91%, 98%, and 91.2% using DFL for detecting cyberattacks and classifying MNIST and CIFAR-10, respectively, reducing training time by 32% compared to centralized approaches.

翻译：2016年，谷歌提出了联邦学习（Federated Learning, FL）作为一种新颖范式，旨在在保护数据隐私的前提下，跨联邦参与者训练机器学习（Machine Learning, ML）模型。自诞生以来，集中式联邦学习（Centralized FL, CFL）一直是最常用的方法，其中中央实体聚合参与者模型以生成全局模型。然而，CFL存在通信瓶颈、单点故障以及对中央服务器的依赖等局限性。去中心化联邦学习（Decentralized Federated Learning, DFL）通过实现去中心化模型聚合并最小化对中央实体的依赖，解决了这些问题。尽管取得了这些进展，当前训练DFL模型的平台仍面临关键挑战，例如管理异构联邦网络拓扑。为克服这些挑战，本文提出了Fedstellar，一个新颖的平台，旨在以去中心化、半去中心化和集中式方式，跨物理或虚拟化设备的多样化联邦训练FL模型。Fedstellar的实现包括一个具有交互式图形界面的Web应用程序、一个用于部署物理或虚拟设备节点联邦的控制器，以及部署在每个设备上的核心组件，提供网络中训练、聚合和通信所需逻辑。该平台的有效性已在两个场景中得到验证：涉及单板设备（如树莓派）检测网络攻击的物理部署，以及使用MNIST和CIFAR-10数据集在受控环境中比较多种FL方法的虚拟化部署。在两个场景中，Fedstellar均展现出稳定性能和适应性：在检测网络攻击以及分类MNIST和CIFAR-10时，使用DFL分别达到91%、98%和91.2%的F1分数，同时相较于集中式方法将训练时间减少了32%。