{
  "id": "2302.01326",
  "version": "v1",
  "published": "2023-02-02T18:56:24.000Z",
  "updated": "2023-02-02T18:56:24.000Z",
  "title": "Federated Analytics: A survey",
  "authors": [
    "Ahmed Roushdy Elkordy",
    "Yahya H. Ezzeldin",
    "Shanshan Han",
    "Shantanu Sharma",
    "Chaoyang He",
    "Sharad Mehrotra",
    "Salman Avestimehr"
  ],
  "comment": "To appear in APSIPA Transactions on Signal and Information Processing, Volume 12, Issue 1",
  "journal": "APSIPA Transactions on Signal and Information Processing, Volume 12, Issue 1, 2023",
  "categories": [
    "cs.LG",
    "cs.CR"
  ],
  "abstract": "Federated analytics (FA) is a privacy-preserving framework for computing data analytics over multiple remote parties (e.g., mobile devices) or silo-ed institutional entities (e.g., hospitals, banks) without sharing the data among parties. Motivated by the practical use cases of federated analytics, we follow a systematic discussion on federated analytics in this article. In particular, we discuss the unique characteristics of federated analytics and how it differs from federated learning. We also explore a wide range of FA queries and discuss various existing solutions and potential use case applications for different FA queries.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-02-02T18:56:24.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "federated analytics",
      "fa queries",
      "multiple remote parties",
      "case applications",
      "computing data analytics"
    ],
    "tags": [
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}