Federated Transfer Learning

Federated Transfer Learning is a machine learning framework that enables knowledge transfer across heterogeneous data distributions in decentralized client settings by preserving data locality, thereby combining the principles of federated learning and transfer learning to improve model generalization with limited labeled data.

131

Publications

16.4K

Citations

567

Authors

252

Institutions

Publications per year

2017–2026

131

Variants

Ftl

Authors

567

  1. QY
    Qiang Yang

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

    7 publications · 7.3K citations

    7

    h-index

  2. TC
    Tianjian Chen

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

    4 publications · 7.1K citations

    4

    h-index

  3. XL
    Xiang Li

    Xi'an Jiaotong University, China

    4 publications · 545 citations

    4

    h-index

  4. YT
    Yongxin Tong

    Beihang University, China

    3 publications · 6.5K citations

    3

    h-index

  5. YL
    Yang Liu

    Tsinghua University, China

    3 publications · 6.5K citations

    3

    h-index

1–5 of 567

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 1.7K citations

    13

    h-index

  2. IEEE Access

    6 publications · 334 citations

    6

    h-index

  3. Sensors

    4 publications · 209 citations

    4

    h-index

  4. Knowledge-Based Systems

    3 publications · 105 citations

    3

    h-index

  5. IEEE Internet of Things Journal

    3 publications · 147 citations

    3

    h-index

1–5