Hierarchical Federated Learning

Hierarchical Federated Learning is a federated learning framework that organizes participating devices into multiple layers of intermediary servers to enable localized model aggregation and improved scalability, communication efficiency, and heterogeneity management across distributed clients.

115

Publications

11.4K

Citations

486

Authors

211

Institutions

Publications per year

2017–2026

112

Variants

Hfl

Authors

486

  1. MG
    Mohsen Guizani

    Qatar University, Qatar

    6 publications · 389 citations

    6

    h-index

  2. DN
    Dusit Niyato

    Nanyang Technological University, Singapore

    4 publications · 521 citations

    4

    h-index

  3. TR
    Thippa Reddy Gadekallu

    Vellore Institute of Technology University, India

    4 publications · 993 citations

    4

    h-index

  4. AE
    Aiman Erbad

    Qatar University, Qatar

    4 publications · 340 citations

    4

    h-index

  5. AM
    Amr Mohamed

    Qatar University, Qatar

    4 publications · 340 citations

    4

    h-index

1–5 of 486

Venues

  1. IEEE Internet of Things Journal

    8 publications · 582 citations

    8

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 7 publications · 721 citations

    7

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 276 citations

    7

    h-index

  4. IEEE Transactions on Wireless Communications

    6 publications · 905 citations

    6

    h-index

  5. 4

    h-index

1–5