Federated Learning Privacy

Federated Learning Privacy is a privacy-preserving machine learning paradigm that enables decentralized model training across distributed devices or servers while minimizing data exposure by ensuring raw data remains localized and only model updates are shared.

1K

Publications

77K

Citations

3.1K

Authors

940

Institutions

Publications per year

2017–2026

1K

Authors

3.1K

  1. HL
    Hongwei Li

    University of Electronic Science and Technology of China, China

    13 publications · 2.6K citations

    14

    h-index

  2. SY
    Shui Yu

    University of Technology Sydney, Australia

    12 publications · 1.3K citations

    13

    h-index

  3. QY
    Qiang Yang

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

    13 publications · 1.6K citations

    13

    h-index

  4. GX
    Guowen Xu

    University of Electronic Science and Technology of China, China

    13 publications · 2.4K citations

    13

    h-index

  5. RL
    Rongxing Lu

    University of New Brunswick, Canada

    10 publications · 853 citations

    12

    h-index

1–5 of 3.1K

Venues

  1. arXiv (Cornell University)

    Repository · 107 publications · 7.5K citations

    36

    h-index

  2. IEEE Internet of Things Journal

    40 publications · 4.8K citations

    32

    h-index

  3. IEEE Transactions on Industrial Informatics

    27 publications · 3.9K citations

    25

    h-index

  4. IEEE Access

    28 publications · 2.3K citations

    24

    h-index

  5. 22

    h-index

1–5