Federated Recommendation

Federated Recommendation is a recommendation paradigm that enables collaborative model training across decentralized user devices or data silos while preserving privacy by avoiding direct data sharing, thereby distinguishing itself from centralized recommendation approaches through its distributed learning architecture and privacy-preserving data locality principle.

126

Publications

6K

Citations

520

Authors

211

Institutions

Publications per year

2017–2026

124

Variants

Federated Recommender Systems

Authors

520

  1. HY
    Hongzhi Yin

    The University of Queensland, Australia

    11 publications · 703 citations

    11

    h-index

  2. FW
    Fangzhao Wu

    Tsinghua University, China

    9 publications · 450 citations

    9

    h-index

  3. XX
    Xing Xie

    Microsoft Research Asia (China), China

    6 publications · 251 citations

    6

    h-index

  4. YH
    Yongfeng Huang

    Tsinghua University, China

    5 publications · 234 citations

    5

    h-index

  5. CW
    Chuhan Wu

    Tsinghua University, China

    5 publications · 234 citations

    5

    h-index

1–5 of 520

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 370 citations

    10

    h-index

  2. IEEE Transactions on Consumer Electronics

    7 publications · 445 citations

    7

    h-index

  3. ACM Transactions on Information Systems

    4 publications · 242 citations

    4

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 262 citations

    4

    h-index

  5. IEEE Internet of Things Journal

    5 publications · 140 citations

    4

    h-index

1–5