{
  "id": "1902.01046",
  "version": "v1",
  "published": "2019-02-04T06:27:41.000Z",
  "updated": "2019-02-04T06:27:41.000Z",
  "title": "Towards Federated Learning at Scale: System Design",
  "authors": [
    "Keith Bonawitz",
    "Hubert Eichner",
    "Wolfgang Grieskamp",
    "Dzmitry Huba",
    "Alex Ingerman",
    "Vladimir Ivanov",
    "Chloe Kiddon",
    "Jakub Konecny",
    "Stefano Mazzocchi",
    "H. Brendan McMahan",
    "Timon Van Overveldt",
    "David Petrou",
    "Daniel Ramage",
    "Jason Roselander"
  ],
  "categories": [
    "cs.LG",
    "cs.DC",
    "stat.ML"
  ],
  "abstract": "Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data. We have built a scalable production system for Federated Learning in the domain of mobile devices, based on TensorFlow. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, and touch upon the open problems and future directions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-02-04T06:27:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "federated learning",
      "system design",
      "open problems",
      "large corpus",
      "distributed machine learning approach"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}