Decentralized Learning

Decentralized Learning is a multi-robot learning paradigm in which individual robots independently update their policies or models using locally available data and inter-robot communication, without reliance on a central coordinator to aggregate information or synchronize learning.

368

Publications

19.4K

Citations

1.1K

Authors

420

Institutions

Publications per year

2017–2026

259

Authors

1.1K

  1. TB
    Tamer Başar

    University of Illinois Urbana-Champaign, United States

    10 publications · 754 citations

    10

    h-index

  2. AR
    Alejandro Ribeiro

    University of Pennsylvania, United States

    10 publications · 303 citations

    10

    h-index

  3. ZY
    Zhuoran Yang

    Princeton University, United States

    9 publications · 798 citations

    9

    h-index

  4. KZ
    Kaiqing Zhang

    University of Illinois Urbana-Champaign, United States

    8 publications · 719 citations

    8

    h-index

  5. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    8 publications · 221 citations

    8

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 43 publications · 1.6K citations

    19

    h-index

  2. IEEE Transactions on Signal Processing

    12 publications · 901 citations

    12

    h-index

  3. IEEE Internet of Things Journal

    8 publications · 645 citations

    8

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 184 citations

    7

    h-index

  5. IEEE Transactions on Vehicular Technology

    5 publications · 446 citations

    5

    h-index

1–5