Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization

Harlin Lee, Andrea L. Bertozzi, Jelena Kovacevic, Yuejie Chi. Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022. pages 5947-5951, IEEE, 2022. [doi]

@inproceedings{LeeBKC22,
  title = {Privacy-Preserving Federated Multi-Task Linear Regression: A One-Shot Linear Mixing Approach Inspired By Graph Regularization},
  author = {Harlin Lee and Andrea L. Bertozzi and Jelena Kovacevic and Yuejie Chi},
  year = {2022},
  doi = {10.1109/ICASSP43922.2022.9746007},
  url = {https://doi.org/10.1109/ICASSP43922.2022.9746007},
  researchr = {https://researchr.org/publication/LeeBKC22},
  cites = {0},
  citedby = {0},
  pages = {5947-5951},
  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022},
  publisher = {IEEE},
  isbn = {978-1-6654-0540-9},
}