{
  "id": "1905.06731",
  "version": "v1",
  "published": "2019-05-16T13:23:49.000Z",
  "updated": "2019-05-16T13:23:49.000Z",
  "title": "BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning",
  "authors": [
    "Abhijit Guha Roy",
    "Shayan Siddiqui",
    "Sebastian Pölsterl",
    "Nassir Navab",
    "Christian Wachinger"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult for an individual medical center to reach large enough sample sizes to build their own, personalized models. As an alternative, data from all centers could be pooled to train a centralized model that everyone can use. However, such a strategy is often infeasible due to the privacy-sensitive nature of medical data. Recently, federated learning (FL) has been introduced to collaboratively learn a shared prediction model across centers without the need for sharing data. In FL, clients are locally training models on site-specific datasets for a few epochs and then sharing their model weights with a central server, which orchestrates the overall training process. Importantly, the sharing of models does not compromise patient privacy. A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients. In this paper, we introduce BrainTorrent, a new FL framework without a central server, particularly targeted towards medical applications. BrainTorrent presents a highly dynamic peer-to-peer environment, where all centers directly interact with each other without depending on a central body. We demonstrate the overall effectiveness of FL for the challenging task of whole brain segmentation and observe that the proposed server-less BrainTorrent approach does not only outperform the traditional server-based one but reaches a similar performance to a model trained on pooled data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-05-16T13:23:49.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "decentralized federated learning",
      "braintorrent",
      "central server",
      "central body",
      "training process"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}