Federated Optimization

Federated Optimization is an optimization framework in machine learning that coordinates model parameter updates across decentralized devices with local data, preserving data locality while minimizing a global objective through collaborative training.

287

Publications

24.3K

Citations

883

Authors

323

Institutions

Publications per year

2017–2026

283

Authors

883

  1. PR
    Peter Richtárik

    University of Edinburgh, United Kingdom

    9 publications · 2K citations

    9

    h-index

  2. YJ
    Yaochu Jin

    University of Surrey, United Kingdom

    5 publications · 202 citations

    7

    h-index

  3. GJ
    Gauri Joshi

    Carnegie Mellon University, United States

    6 publications · 1.2K citations

    6

    h-index

  4. JW
    Jianyu Wang

    Carnegie Mellon University, United States

    5 publications · 1K citations

    5

    h-index

  5. FH
    Filip Hanzely

    King Abdullah University of Science and Technology, Saudi Arabia

    5 publications · 285 citations

    5

    h-index

1–5 of 883

Venues

  1. arXiv (Cornell University)

    Repository · 62 publications · 7.6K citations

    28

    h-index

  2. IEEE Transactions on Wireless Communications

    9 publications · 604 citations

    12

    h-index

  3. IEEE Internet of Things Journal

    8 publications · 489 citations

    9

    h-index

  4. IEEE Transactions on Signal Processing

    7 publications · 718 citations

    7

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 921 citations

    6

    h-index

1–5