Personalized federated learning (PFL) is an exciting approach that allows machine learning
(ML) models to be trained on diverse and decentralized sources of data, while maintaining …

Advances in the new generation of Internet of Things (IoT) technology are propelling the
growth of intelligent industrial applications worldwide. Simultaneously, widespread adoption …

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world
intelligent systems. Despite the emergence of an increasing number of OOD detection …

Federated learning has gained recognitions as a secure approach for safeguarding local
private data in collaborative learning. But the advent of gradient inversion research has …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

Computer Vision (CV) is playing a significant role in transforming society by utilizing
machine learning (ML) tools for a wide range of tasks. However, the need for large-scale …

Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …

Federated learning facilitates collaborative learning among multiple parties while ensuring
client privacy. The vulnerability of federated learning to diverse Byzantine attacks stems from …

The main premise of federated learning (FL) is that machine learning model updates are
computed locally to preserve user data privacy. This approach avoids by design user data to …

The development of artificial intelligence (AI) and self-driving technology is expected to
enhance intelligent transportation systems (ITSs) by improving road safety and mobility …