Collaborative Inference

Collaborative Inference is a distributed inferencing paradigm in edge artificial intelligence whereby multiple edge devices or nodes cooperatively execute model inference by sharing computational tasks, intermediate representations, or predictions to enhance accuracy, efficiency, or scalability, distinguishing it from isolated or centralized inference approaches.

83

Publications

5.4K

Citations

298

Authors

117

Institutions

Publications per year

2017–2026

57

Authors

298

  1. AH
    Ali H. Sayed

    University of California, Los Angeles, United States

    5 publications · 195 citations

    5

    h-index

  2. AD
    Anne Deiglmayr

    ETH Zurich, Switzerland

    3 publications · 67 citations

    3

    h-index

  3. XQ
    Xiuquan Qiao

    Beijing University of Posts and Telecommunications, China

    3 publications · 89 citations

    3

    h-index

  4. EL
    En Li

    Sun Yat-sen University, China

    3 publications · 1.3K citations

    3

    h-index

  5. YH
    Yakun Huang

    Beijing University of Posts and Telecommunications, China

    3 publications · 89 citations

    3

    h-index

1–5 of 298

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 264 citations

    7

    h-index

  2. IEEE Transactions on Mobile Computing

    TMCCI · 6 publications · 232 citations

    5

    h-index

  3. IEEE Internet of Things Journal

    5 publications · 281 citations

    5

    h-index

  4. IEEE Transactions on Signal Processing

    4 publications · 271 citations

    4

    h-index

  5. 2

    h-index

1–5