Personalized Federated Learning

Personalized Federated Learning is a federated learning paradigm that extends standard federated learning by enabling participant-specific model personalization to address heterogeneity in data distributions, thereby optimizing individual performance while leveraging shared global knowledge.

205

Publications

14.5K

Citations

813

Authors

282

Institutions

Publications per year

2017–2026

204

Variants

Pfl

Authors

813

  1. XC
    Xu Chen

    Sun Yat-sen University, China

    5 publications · 972 citations

    5

    h-index

  2. SG
    Song Guo

    Hong Kong Polytechnic University, Hong Kong SAR China

    5 publications · 324 citations

    5

    h-index

  3. JW
    Jindong Wang

    Microsoft Research Asia (China), China

    5 publications · 1.2K citations

    5

    h-index

  4. HG
    Haibing Guan

    Shanghai Jiao Tong University, China

    4 publications · 530 citations

    4

    h-index

  5. HY
    Hua Yang

    Queen's University Belfast, United Kingdom

    4 publications · 530 citations

    4

    h-index

1–5 of 813

Venues

  1. arXiv (Cornell University)

    Repository · 49 publications · 2.8K citations

    24

    h-index

  2. IEEE Internet of Things Journal

    7 publications · 598 citations

    7

    h-index

  3. 6

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 1.4K citations

    6

    h-index

  5. 5

    h-index

1–5