Breaking State-of-the-Art Poisoning Defenses to Federated Learning: An Optimization-Based Attack Framework.
Yuxin Yang|Qiang Li|Chenfei Nie|Yuan Hong|Binghui Wang
| Anthology ID: | DBLP:conf/cikm/Yang0NHW24 |
|---|---|
| Volume: | Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024 |
| Year: | 2024 |
| Venue: | International Conference on Information and Knowledge Management (CIKM) |
| Publisher: | ACM |
| Pages: | 2930-2939 |
| URL: | https://doi.org/10.1145/3627673.3679566 |
| DOI: | https://doi.org/10.1145/3627673.3679566 |
| DBLP: | conf/cikm/Yang0NHW24 |
| BibTeX: |
@inproceedings{yang-2024-breaking,
author = {Yuxin Yang and
Qiang Li and
Chenfei Nie and
Yuan Hong and
Binghui Wang},
editor = {Edoardo Serra and
Francesca Spezzano},
title = {{Breaking State-of-the-Art Poisoning Defenses to Federated Learning: An Optimization-Based Attack Framework}},
booktitle = {{Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024, Boise, ID, USA, October 21-25, 2024}},
pages = {2930--2939},
publisher = {ACM},
year = {2024},
url = {https://doi.org/10.1145/3627673.3679566},
doi = {https://doi.org/10.1145/3627673.3679566}
}