Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Medical image analysis using deep neural networks (DNN) has demonstrated state-of-the-
art performance in image classification and segmentation tasks, aiding disease diagnosis …

Artificial intelligence (AI) has led to a high rate of development in healthcare, and good
progress has been made on many complex medical problems. However, there is a lack of …

Recently, model compression has been widely used for the deployment of cumbersome
deep models on resource-limited edge devices in the performance-demanding industrial …

Abstract Machine learning in medical imaging often faces a fundamental dilemma, namely,
the small sample size problem. Many recent studies suggest using multi-domain data …

In this work, we propose GPT-FL, a generative pre-trained model-assisted federated
learning (FL) framework. At its core, GPT-FL leverages generative pre-trained models to …

Aviation Internet of Things (AIoT) data sharing can create tremendous value for participants.
With the development of AIoT and intelligent civil aviation, data security and privacy …

Federated learning (FL) algorithm has been widely studied in recent years due to its ability
for sharing data while protecting privacy. However, FL has risks, such as model inversion …

The sharing of flight operation data brings huge benefits to all participants, but for the privacy
protection and data security, it is difficult to directly share flight operation data. Federated …

Federated Learning (FL) enabled the reliability and robustness of 5G communication
networks for wireless edge computing to provide collaborative Deep Learning (DL) of …