The practical needs of the" right to be forgotten" and poisoned data removal call for efficient
machine unlearning techniques, which enable machine learning models to unlearn, or to …

Large language models (LLMs) have witnessed a meteoric rise in popularity among the
general public users over the past few months, facilitating diverse downstream tasks with …

Numerous machine learning (ML) models have been developed for breast cancer using
various types of data. Successful external validation (EV) of ML models is important …

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 …

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made
revolutionary progress in recent years, and are widely used in various fields. The high …

On-device ML is increasingly used in different applications. It brings convenience to offline
tasks and avoids sending user-private data through the network. On-device ML models are …

Aiming at reducing the large distortion of the existing reversible watermarking for three-
dimensional (3D) mesh models and meeting the needs of authentication in cloud-based …

Deep learning (DL) has achieved tremendous success in recent years in many fields. The
success of DL typically relies on a considerable amount of training data and the expensive …

Despite its enormous economical and societal impact, lack of human-perceived control and
safety is re-defining the design and development of emerging AI-based technologies. New …

Mobile-edge computing (MEC) raises the problem of selfish user devices that utilize less
computing resources than expected to execute offloading tasks or maliciously discard …