CVPR · 2022 · Conference paper · Top venue

Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning

Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel L. Rubin

Stanford University · University of California, Santa Cruz · Carnegie Mellon University · Johns Hopkins University

Rendered by arXiv from the LaTeX source. If anything looks wrong, switch to the PDF.HTML · PDF · Open in a new tab ↗ · Close

Published in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Date: 2022-06-01
Citations: 240
arXiv: 2106.06047 ↗

Cite

@inproceedings{qu2022rethinking,
  title = {Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learning},
  author = {Liangqiong Qu and Yuyin Zhou and Paul Pu Liang and Yingda Xia and Feifei Wang and Ehsan Adeli and Li Fei-Fei and Daniel L. Rubin},
  year = {2022},
  booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  eprint = {2106.06047},
  archivePrefix = {arXiv},
  doi = {10.1109/CVPR52688.2022.00982},
  url = {https://arxiv.org/abs/2106.06047},
}