Federated learning (FL) has become a popular mode of learning, allowing model training
without the need to share data. Unfortunately, it remains vulnerable to privacy leakage and …

This paper jointly considers privacy preservation and Byzantine-robustness in decentralized
learning. In a decentralized network, honest-but-curious agents faithfully follow the …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated learning (FL) enables collaborative model training without sharing private data,
thereby potentially meeting the growing demand for data privacy protection. Despite its …

The flourishing of intelligent connected vehicles (ICVs) has fostered the emergence of
vehicular crowdsourcing (VCS) applications, in which ICVs function as workers to execute …

Federated learning (FL) has recently gained significant momentum due to its potential to
leverage large-scale distributed user data while preserving user privacy. However, the …

In this research, a federated learning-based poisoning attack recognition and prevention
framework has been developed. Initially, the required data to perform data poison attack …

Bidirectional privacy-preservation federated learning is crucial as both local gradients and
the global model may leak privacy. However, only a few works attempt to achieve it, and they …