Publications
Comments on “Privacy-Enhanced Federated Learning Against Poisoning Adversaries”
| Author | Schneider, Thomas; Suresh, Ajith; Yalame, Hossein |
|---|---|
| Date | 2023 |
| Type | Journal Article |
| Abstract | Liu et al. (2021) recently proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic encryption. In this article, we show that PEFL does not preserve privacy. In particular, we illustrate that PEFL reveals the entire gradient vector of all users in clear to one of the participating entities, thereby violating privacy. Furthermore, we clearly show that an immediate fix for this issue is still insufficient to achieve privacy by pointing out multiple flaws in the proposed system. |
| ISSN | 1556-6013 |
| In | IEEE Transactions on Information Forensics and Security, p.1407-1409 |
| Publisher | IEEE |
| Url | https://tubiblio.ulb.tu-darmstadt.de/id/eprint/137241 |
