Federated learning has emerged as an effective paradigm to achieve privacy-preserving
collaborative learning among different parties. Compared to traditional centralized learning …

The rapid advancement of large language models (LLMs) has revolutionized artificial
intelligence, introducing unprecedented capabilities in natural language processing and …

An off-the-shelf model as a commercial service could be stolen by model stealing attacks,
posing great threats to the rights of the model owner. Model fingerprinting aims to verify …

Measuring similarity of neural networks to understand and improve their behavior has
become an issue of great importance and research interest. In this survey, we provide a …

Transfer learning is a popular software reuse technique in the deep learning community that
enables developers to build custom models (students) based on sophisticated pretrained …

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 …

The last decade has witnessed the success of deep learning and the surge of publicly
released trained models, which necessitates the quantification of the model functional …

Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

Deep learning has been widely applied in solving many tasks, such as image recognition,
speech recognition, and natural language processing. It requires a high-quality dataset …

In recent years, many model intellectual property (IP) proof methods for IP protection have
been proposed, such as model watermarking and model fingerprinting. However, with the …