Client Selection

Client Selection is a strategic process in federated learning that determines which participating devices or data providers are chosen for model training in each communication round, distinguishing it from other resource allocation mechanisms by its focus on optimizing performance, fairness, and efficiency under constraints such as heterogeneity, availability, and communication costs.

188

Publications

11K

Citations

602

Authors

252

Institutions

Publications per year

2017–2026

139

Authors

602

  1. HQ
    Hua Qian

    Chinese Academy of Sciences, China

    5 publications · 296 citations

    5

    h-index

  2. WL
    Weiwei Lin

    South China University of Technology, China

    5 publications · 346 citations

    5

    h-index

  3. HZ
    Hongbin Zhu

    ShanghaiTech University, China

    5 publications · 296 citations

    5

    h-index

  4. MG
    Mohsen Guizani

    Qatar University, Qatar

    5 publications · 178 citations

    5

    h-index

  5. AM
    Azzam Mourad

    Lebanese American University, Lebanon

    4 publications · 392 citations

    4

    h-index

1–5 of 602

Venues

  1. IEEE Internet of Things Journal

    18 publications · 1.3K citations

    18

    h-index

  2. arXiv (Cornell University)

    Repository · 12 publications · 491 citations

    12

    h-index

  3. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 7 publications · 820 citations

    7

    h-index

  4. IEEE Transactions on Wireless Communications

    5 publications · 637 citations

    4

    h-index

  5. IEEE Access

    4 publications · 327 citations

    4

    h-index

1–5