Parameter Server Architecture

Parameter Server Architecture is a distributed computing framework that coordinates the parallel training of machine learning models by decoupling model parameters and computation across parameter servers and worker nodes, enabling scalable and efficient optimization in large-scale data settings.

252

Publications

34.1K

Citations

849

Authors

257

Institutions

Publications per year

2017–2026

233

Authors

849

  1. DG
    Denız Gündüz

    Imperial College London, United Kingdom

    16 publications · 1.9K citations

    14

    h-index

  2. CW
    Chuan Wu

    University of Hong Kong, Hong Kong SAR China

    11 publications · 715 citations

    11

    h-index

  3. MM
    Mohammad Mohammadi Amiri

    Imperial College London, United Kingdom

    9 publications · 1.6K citations

    9

    h-index

  4. SG
    Song Guo

    Hong Kong Polytechnic University, Hong Kong SAR China

    8 publications · 801 citations

    8

    h-index

  5. YZ
    Yipeng Zhou

    Macquarie University, Australia

    8 publications · 286 citations

    8

    h-index

1–5 of 849

Venues

  1. arXiv (Cornell University)

    Repository · 40 publications · 10.7K citations

    23

    h-index

  2. IEEE Journal on Selected Areas in Communications

    11 publications · 788 citations

    11

    h-index

  3. IEEE Transactions on Wireless Communications

    9 publications · 377 citations

    9

    h-index

  4. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 8 publications · 245 citations

    8

    h-index

  5. IEEE Internet of Things Journal

    8 publications · 1K citations

    7

    h-index

1–5