Comments on “Privacy-Enhanced Federated Learning Against Poisoning Adversaries”
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2023.3238544
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… read more here.
Keywords: comments privacy; enhanced federated; federated learning; learning poisoning ... See more keywords
RobustFL: Robust Federated Learning Against Poisoning Attacks in Industrial IoT Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Industrial Informatics"
DOI: 10.1109/tii.2021.3132954
Abstract: Industrial Internet of Things (IIoT) systems are key enabling infrastructures that sustain the functioning of production and manufacturing. To satisfy the intelligence demands, federated learning has been envisioned as a promising technique for IIoT applications… read more here.
Keywords: robust federated; federated learning; learning poisoning; model ... See more keywords