Large language models (LLMs) have demonstrated extraordinary capabilities and
contributed to multiple fields, such as generating and summarizing text, language …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

The rapid advancement and widespread use of large language models (LLMs) have raised
significant concerns regarding the potential leakage of personally identifiable information …

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-
generated content (AIGC) has garnered increasing attention and is leading a paradigm shift …

A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the
generalization of the global model on each client. To address this, we propose a method …

To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering
significant advantages in agility, responsiveness, and potential environmental benefits. The …

The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …