FLock: Robust and Privacy-Preserving Federated Learning based on Practical Blockchain State Channels.
Ruonan Chen|Ye Dong|Yizhong Liu|Tingyu Fan|Dawei Li|Zhenyu Guan|Jianwei Liu|Jianying Zhou
| Anthology ID: | DBLP:conf/www/ChenDLF0G0025 |
|---|---|
| Volume: | Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025 |
| Year: | 2025 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 884-895 |
| URL: | https://doi.org/10.1145/3696410.3714666 |
| DOI: | https://doi.org/10.1145/3696410.3714666 |
| DBLP: | conf/www/ChenDLF0G0025 |
| BibTeX: |
@inproceedings{chen-2025-flock,
author = {Ruonan Chen and
Ye Dong and
Yizhong Liu and
Tingyu Fan and
Dawei Li and
Zhenyu Guan and
Jianwei Liu and
Jianying Zhou},
editor = {Guodong Long and
Michale Blumestein and
Yi Chang and
Liane Lewin-Eytan and
Zi Huang and
Elad Yom-Tov},
title = {{FLock: Robust and Privacy-Preserving Federated Learning based on Practical Blockchain State Channels}},
booktitle = {{Proceedings of the ACM on Web Conference 2025, WWW 2025, Sydney, NSW, Australia, 28 April 2025- 2 May 2025}},
pages = {884--895},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3696410.3714666},
doi = {https://doi.org/10.1145/3696410.3714666}
}