Federated Reinforcement Learning

Federated Reinforcement Learning is a machine learning framework that integrates reinforcement learning with federated learning principles to enable decentralized agents to collaboratively learn shared control policies while preserving data locality and privacy.

162

Publications

10.3K

Citations

620

Authors

276

Institutions

Publications per year

2017–2026

162

Authors

620

  1. MB
    Mehdi Bennis

    University of Oulu, Finland

    9 publications · 352 citations

    9

    h-index

  2. MG
    Mohsen Guizani

    Qatar University, Qatar

    6 publications · 285 citations

    6

    h-index

  3. AL
    Abdullah Lakhan

    University of Anbar, Iraq

    5 publications · 235 citations

    5

    h-index

  4. NK
    Neeraj Kumar

    Thapar Institute of Engineering & Technology, India

    4 publications · 199 citations

    4

    h-index

  5. FT
    Fengxiao Tang

    Tohoku University, Japan

    4 publications · 217 citations

    4

    h-index

1–5 of 620

Venues

  1. IEEE Internet of Things Journal

    18 publications · 1.1K citations

    16

    h-index

  2. IEEE Transactions on Vehicular Technology

    10 publications · 753 citations

    10

    h-index

  3. 8

    h-index

  4. arXiv (Cornell University)

    Repository · 9 publications · 276 citations

    8

    h-index

  5. IEEE Transactions on Industrial Informatics

    8 publications · 830 citations

    8

    h-index

1–5