{
  "id": "2306.09750",
  "version": "v1",
  "published": "2023-06-16T10:34:49.000Z",
  "updated": "2023-06-16T10:34:49.000Z",
  "title": "Fedstellar: A Platform for Decentralized Federated Learning",
  "authors": [
    "Enrique Tomás Martínez Beltrán",
    "Ángel Luis Perales Gómez",
    "Chao Feng",
    "Pedro Miguel Sánchez Sánchez",
    "Sergio López Bernal",
    "Gérôme Bovet",
    "Manuel Gil Pérez",
    "Gregorio Martínez Pérez",
    "Alberto Huertas Celdrán"
  ],
  "categories": [
    "cs.LG",
    "cs.AI",
    "cs.DC",
    "cs.NI"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-06-16T10:34:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "decentralized federated learning",
      "fedstellar",
      "heterogeneous federation network topologies",
      "central entity",
      "platforms training dfl models struggle"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}