Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Deep neural networks (DNNs) are currently being deployed as machine learning technology
in a wide range of important real-world applications. DNNs consist of a huge number of …

Understanding to what extent neural networks memorize training data is an intriguing
question with practical and theoretical implications. In this paper we show that in some …

The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities
has spurred public curiosity to evaluate and compare different LLMs, leading many …

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious
data, posing risks of privacy breaches, security vulnerabilities, and performance …

Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Multi-Satellite, multi-modality in-orbit fusion is a challenging task as it explores the fusion
representation of complex high-dimensional data under limited computational resources …

With the rapid development of imaging sensor technology in the field of remote sensing,
multi-modal remote sensing data fusion has emerged as a crucial research direction for land …

Recent studies have shown that the training samples can be recovered from gradients,
which are called Gradient Inversion (GradInv) attacks. However, there remains a lack of …