Decentralized Federated Learning

Decentralized Federated Learning is a federated learning paradigm in which participants collaboratively train a global model without relying on a central server, distinguished by its fully peer-to-peer architecture for model aggregation and parameter synchronization.

269

Publications

24.4K

Citations

1.1K

Authors

410

Institutions

Publications per year

2017–2026

265

Variants

Decentralized Fl

Authors

1.1K

  1. DN
    Dusit Niyato

    Nanyang Technological University, Singapore

    7 publications · 890 citations

    7

    h-index

  2. HV
    H. Vincent Poor

    Princeton University, United States

    6 publications · 599 citations

    6

    h-index

  3. ZX
    Zehui Xiong

    Nanyang Technological University, Singapore

    6 publications · 872 citations

    6

    h-index

  4. JK
    Jiawen Kang

    Guangdong University of Technology, China

    5 publications · 856 citations

    5

    h-index

  5. JL
    Jun Li

    Nanjing University of Science and Technology, China

    5 publications · 487 citations

    5

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 41 publications · 8.4K citations

    24

    h-index

  2. IEEE Internet of Things Journal

    13 publications · 1.4K citations

    13

    h-index

  3. IEEE Transactions on Industrial Informatics

    6 publications · 749 citations

    6

    h-index

  4. IEEE Access

    6 publications · 864 citations

    6

    h-index

  5. IEEE Network

    6 publications · 895 citations

    6

    h-index

1–5