Distributed Machine Learning

Distributed Machine Learning is a subfield of Scalable Machine Learning that leverages distributed computing systems to parallelize and coordinate the training and inference processes of machine learning models across multiple interconnected nodes or devices, thereby enabling efficient processing of large-scale datasets and models.

2.1K

Publications

171.1K

Citations

6.6K

Authors

1.5K

Institutions

Publications per year

2017–2026

1.5K

Authors

6.6K

  1. ZH
    Zhu Han

    University of Houston, United States

    18 publications · 2.1K citations

    17

    h-index

  2. HV
    H. Vincent Poor

    Princeton University, United States

    18 publications · 3.7K citations

    16

    h-index

  3. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    22 publications · 2K citations

    16

    h-index

  4. QH
    Qirong Ho

    Carnegie Mellon University, United States

    17 publications · 1.3K citations

    15

    h-index

  5. MJ
    Martin Jaggi

    École Polytechnique Fédérale de Lausanne, Switzerland

    20 publications · 1.4K citations

    15

    h-index

1–5 of 6.6K

Venues

  1. arXiv (Cornell University)

    Repository · 354 publications · 37.7K citations

    74

    h-index

  2. IEEE Transactions on Signal Processing

    40 publications · 3.9K citations

    25

    h-index

  3. IEEE Internet of Things Journal

    32 publications · 3.3K citations

    24

    h-index

  4. IEEE Access

    37 publications · 1.5K citations

    23

    h-index

  5. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 28 publications · 2.4K citations

    20

    h-index

1–5