Machine learning (ML) models have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Machine learning (ML) has gained widespread adoption in a variety of fields, including
computer vision and natural language processing. However, ML models are vulnerable to …

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-
preservation demands in artificial intelligence. As machine learning, federated learning is …

The Internet of Things (IoT) consists of a massive number of smart devices capable of data
collection, storage, processing, and communication. The adoption of the IoT has brought …

Federated Learning is a growing branch of Artificial Intelligence with the wide usage of
mobile computing and IoT technologies. Since this technology uses distributed computing …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

As the deployment of pre-trained language models (PLMs) expands, pressing security
concerns have arisen regarding the potential for malicious extraction of training data, posing …

Federated learning (FL) has achieved state-of-the-art performance in distributed learning
tasks with privacy requirements. However, it has been discovered that FL is vulnerable to …

With the development of machine learning techniques, the attention of research has been
moved from single-modal learning to multi-modal learning, as real-world data exist in the …

Federated learning is an established method for training machine learning models without
sharing training data. However, recent work has shown that it cannot guarantee data privacy …