Gradient Synchronization

Gradient Synchronization is a data parallelism technique that belongs to the class of distributed optimization methods, specifically distinguished by its coordination of gradient updates across multiple processing nodes to ensure model consistency during synchronous training.

48

Publications

3.2K

Citations

164

Authors

55

Institutions

Publications per year

2017–2026

32

Authors

164

  1. CY
    Cheng Yang

    Tsinghua University, China

    4 publications · 74 citations

    4

    h-index

  2. FK
    Fabian Kühn

    ETH Zurich, Switzerland

    3 publications · 73 citations

    3

    h-index

  3. TL
    Thomas Locher

    ETH Zurich, Switzerland

    3 publications · 73 citations

    3

    h-index

  4. FY
    Feng Yan

    University of Nevada, Reno, United States

    3 publications · 854 citations

    3

    h-index

  5. DL
    Dan Li

    Tsinghua University, China

    3 publications · 64 citations

    3

    h-index

1–5 of 164

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 1.2K citations

    10

    h-index

  2. Neural Information Processing Systems

    Conference · 3 publications · 51 citations

    3

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 441 citations

    2

    h-index

  4. IBM Journal of Research and Development

    1 publications · 10 citations

    1

    h-index

  5. Theory of Computing Systems

    1 publications · 20 citations

    1

    h-index

1–5