Federated Learning Theory

Federated Learning Theory is a theoretical framework within distributed machine learning that formally characterizes the principles, limitations, and guarantees of learning models across decentralized data sources while preserving data locality and privacy.

51

Publications

7.9K

Citations

208

Authors

87

Institutions

Publications per year

2017–2026

51

Authors

208

  1. QY
    Qiang Yang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    3 publications · 269 citations

    3

    h-index

  2. HY
    Han Yu

    Nanyang Technological University, Singapore

    2 publications · 171 citations

    2

    h-index

  3. KL
    Keqiu Li

    Dalian University of Technology, China

    2 publications · 211 citations

    2

    h-index

  4. YS
    Yilong Sun

    Tianjin University of Science and Technology, China

    2 publications · 211 citations

    2

    h-index

  5. YZ
    Yize Zhou

    Hainan University, China

    2 publications · 211 citations

    2

    h-index

1–5 of 208

Venues

  1. arXiv (Cornell University)

    Repository · 12 publications · 297 citations

    11

    h-index

  2. Journal of Information Security and Applications

    JISA · 2 publications · 165 citations

    2

    h-index

  3. IEEE Internet of Things Journal

    2 publications · 77 citations

    2

    h-index

  4. ACM Transactions on Intelligent Systems and Technology

    TIST · 2 publications · 188 citations

    2

    h-index

  5. IEEE Communications Surveys & Tutorials

    ComST · 1 publications · 33 citations

    1

    h-index

1–5