Federated learning (FL) is a privacy-preserving machine learning paradigm that enables
multiple clients to train a unified model without disclosing their private data. However …

Federated learning (FL) is vulnerable to membership inference attacks even it is designed to
protect users' data during model training, as model parameters remember the information of …

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 …

Existing federated learning (FL) designs have been shown to exhibit vulnerabilities which
can be exploited by adversaries to compromise data privacy. However, most current works …

The Poisoning Membership Inference Attack (PMIA) is a newly emerging privacy attack that
poses a significant threat to federated learning (FL). An adversary conducts data poisoning …

Federated Learning (FL) is a machine learning technique that enables multiple parties to
collaboratively train a model using their private datasets. Given its decentralized nature, FL …

Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of
participants to construct a joint ML model without exposing their private training data …

Federated learning (FL) is an emerging technology that allows participants to jointly train a
machine learning model without sharing their private data with others. However, FL is …

Federated learning has lately received great attention for its privacy protection feature.
However, recent researches found that federated learning models are susceptible to various …

Federated learning (FL) is a popular approach to facilitate privacy-aware machine learning
since it allows multiple clients to collaboratively train a global model without granting others …