Communication-efficient Training

Communication-Efficient Training is a methodological framework in distributed machine learning that minimizes inter-node communication overhead during model training while preserving convergence performance through techniques such as gradient compression, sparsification, or infrequent synchronization.

373

Publications

33.6K

Citations

1.2K

Authors

347

Institutions

Publications per year

2017–2026

340

Authors

1.2K

  1. MB
    Mehdi Bennis

    University of Oulu, Finland

    14 publications · 1.1K citations

    14

    h-index

  2. JP
    Jihong Park

    University of Oulu, Finland

    13 publications · 1K citations

    13

    h-index

  3. MJ
    Martin Jaggi

    École Polytechnique Fédérale de Lausanne, Switzerland

    13 publications · 921 citations

    12

    h-index

  4. PR
    Peter Richtárik

    University of Edinburgh, United Kingdom

    10 publications · 5.1K citations

    10

    h-index

  5. SS
    Shaohuai Shi

    Hong Kong Baptist University, Hong Kong SAR China

    9 publications · 544 citations

    9

    h-index

1–5 of 1.2K

Venues

  1. arXiv (Cornell University)

    Repository · 131 publications · 20.1K citations

    46

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 14 publications · 1.2K citations

    14

    h-index

  3. IEEE Internet of Things Journal

    15 publications · 1.1K citations

    14

    h-index

  4. IEEE Journal on Selected Areas in Communications

    10 publications · 720 citations

    9

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 293 citations

    8

    h-index

1–5