Publikationen

Autor	Schneider, Thomas; Suresh, Ajith; Yalame, Hossein
Datum	2023
Art	Journal Article
Abstrakt	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

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