Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.
Yao Zhou|Jun Wu|Haixun Wang|Jingrui He
| Anthology ID: | DBLP:conf/cikm/Zhou0WH22 |
|---|---|
| Volume: | Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022 |
| Year: | 2022 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2753-2762 |
| URL: | https://doi.org/10.1145/3511808.3557232 |
| DOI: | https://doi.org/10.1145/3511808.3557232 |
| DBLP: | conf/cikm/Zhou0WH22 |
| BibTeX: |
@inproceedings{zhou-2022-adversarial,
author = {Yao Zhou and
Jun Wu and
Haixun Wang and
Jingrui He},
editor = {Mohammad Al Hasan and
Li Xiong},
title = {{Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning}},
booktitle = {{Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA, October 17-21, 2022}},
pages = {2753--2762},
publisher = {ACM},
year = {2022},
url = {https://doi.org/10.1145/3511808.3557232},
doi = {https://doi.org/10.1145/3511808.3557232}
}