rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Federated learning
    sub title: from algorithms to system implementation
  name:
    name part: Bo, Liefeng
    role:
      role term: creator
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: si
    date issued: 2025
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: xvii, 527 pages : illustrations.
  abstract: "Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorithms and finally system implementation in three parts. It provides a comprehensive review and summary of federated learning technology, as well as presenting numerous novel federated learning algorithms which no other books have summarized. The work also references the most recent papers, articles and reviews from the past several years to keep pace with the academic and industrial state of the art of federated learning. The first part lays a foundational understanding of federated learning by going through its definition and characteristics, and also possible application scenarios and related privacy protection technologies. The second part elaborates on some of the federated learning algorithms innovated by JD Technology which encompass both vertical and horizontal scenarios, including vertical federated tree models, linear regression, kernel learning, asynchronous methods, deep learning, homomorphic encryption, and reinforcement learning. The third and final part shifts in scope to federated learning systems - namely JD Technology's own FedLearn system - by discussing its design and implementation using gRPC, in addition to specific performance optimization techniques plus integration with blockchain technology. This book will serve as a great reference for readers who are experienced in federated learning algorithms, building privacy-preserving machine learning applications or solving real-world problems with privacy-restricted scenarios"--
  note: Liefeng Bo, Heng Huang, Songxiang Gu, Yanqing Chen, JD Technology, China.
  note: Includes bibliographical references and index.
  subject:
    topic: Federated learning (Machine learning)
  subject:
    topic: Machine learning
  classification: Q325.65 .B63 2025
  classification: 006.3/1
  related item:
    identifier: (DLC) 2024018824
    identifier: (OCoLC)1430414449
  identifier: 9789811292552 (electronic bk.)
  identifier: 9811292558 (electronic bk.)
  identifier: 9789811292545
  identifier: 981129254X
  location:
    physical location: POD
  record info:
    record content source: NhCcYBP
    record creation date: 241115
    record change date: 20250810205609.5
    record identifier: in00005657850
    language of cataloging:
      language term: eng