Federated Learning For Robots

Federated Learning for Robots is a distributed machine learning framework in which multiple robotic agents collaboratively train a shared global model while preserving data locality by exchanging model updates rather than raw sensory data.

8

Publications

1K

Citations

32

Authors

17

Institutions

Publications per year

2017–2026

8

Authors

32

  1. XY
    Xianjia Yu

    University of Turku, Finland

    2 publications · 91 citations

    2

    h-index

  2. JP
    Jorge Peña Queralta

    University of Turku, Finland

    2 publications · 91 citations

    2

    h-index

  3. TW
    Tomi Westerlund

    University of Turku, Finland

    2 publications · 91 citations

    2

    h-index

  4. JK
    Ju-Bong Kim

    Korea University of Technology and Education, South Korea

    1 publications · 22 citations

    1

    h-index

  5. RY
    Ruining Yang

    Linyi University, China

    1 publications · 16 citations

    1

    h-index

1–5 of 32

Venues

  1. Procedia Computer Science

    2 publications · 91 citations

    2

    h-index

  2. International Journal of Intelligent Systems

    1 publications · 51 citations

    1

    h-index

  3. IEEE Internet of Things Journal

    1 publications · 722 citations

    1

    h-index

  4. IEEE Robotics and Automation Letters

    1 publications · 87 citations

    1

    h-index

  5. Expert Systems with Applications

    1 publications · 24 citations

    1

    h-index

1–5