Although federated learning offers a level of privacy by aggregating user data without direct
access, it remains inherently vulnerable to various attacks, including poisoning attacks …

Federated learning (FL) is an emerging paradigm of privacy-preserving distributed machine
learning that effectively deals with the privacy leakage problem by utilizing cryptographic …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning
paradigm that aggregates user-trained local gradients into a federated model through a …

As the adoption of Consumer Internet of Things (CIoT) devices surges, so do concerns about
security vulnerabilities and privacy breaches. Given their integration into daily life and data …

Federated learning aggregates data from multiple sources while protecting privacy, which
makes it possible to train efficient models in real scenes. However, although federated …

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 …

Federated learning (FL) allows clients at the edge to learn a shared global model without
disclosing their private data. However, FL is susceptible to poisoning attacks, wherein an …

The use of cryptographic privacy-preserving techniques in Federated Learning (FL)
inadvertently induces a security dilemma because tampered local model parameters are …

In federated learning (FL), multiple clients use local datasets to train models and submit
local gradients to the server for aggregation. However, malicious clients may compromise …