Detecting and localizing anomalies in cyber-physical systems (CPS) has become
increasingly challenging as systems grow in complexity, particularly due to varying sensor …

Federated Learning (FL) has become a widely used approach for training machine learning
models on decentralized data, addressing the significant privacy concerns associated with …

Federated Learning (FL) enables decentralized machine learning while preserving data
privacy. This paper proposes a novel client selection framework that integrates differential …

Federated Learning (FL) is susceptible to poisoning attacks, wherein compromised clients
manipulate the global model by modifying local datasets or sending manipulated model …

Collaborative fairness stands as an essential element in federated learning to encourage
client participation by equitably distributing rewards based on individual contributions …

Federated Learning (FL) has become a popular decentralized learning paradigm to train a
machine learning model using distributed mobile devices without compromising user …

Federated Learning (FL) is increasingly vulnerable to model poisoning attacks, where
malicious clients degrade the global model's accuracy with manipulated updates …

Federated Learning (FL) enables collaborative learning of large-scale distributed clients
without data sharing. However, due to the disparity of computing resources among massive …

In the modern interconnected world, the popularization of networks and the rapid
development of information technology led to the increasing security risks and threats in …

The rise of mobile devices with abundant sensor data and computing power has driven the
trend of federated learning (FL) on them. Personalized FL (PFL) aims to train tailored models …