In the traditional cloud architecture, data needs to be uploaded to the cloud for processing,
bringing delays in transmission and response. Edge network emerges as the times require …

With the rapid development of artificial intelligence (AI), combining machine learning (ML)
and edge computing powered by big data has become a growing trend. However, under …

Driven by technologies such as mobile edge computing and 5G, recent years have
witnessed the rapid development of urban informatics, where a large amount of data is …

Federated learning (FL) can protect clients' privacy from leakage in distributed machine
learning. Applying federated learning to edge computing can protect the privacy of edge …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

With the proliferation of Internet of Things (IoT), zillions of bytes of data are generated at the
network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to …

Federated learning is a recently proposed paradigm that presents significant advantages in
privacy-preserving machine learning services. It enables the deep learning applications on …

Recently, edge computing has attracted significant interest due to its ability to extend cloud
computing utilities and services to the network edge with low response times and …

Edge intelligent computing is widely used in the fields, such as the Internet of Medical
Things (IoMT), which has advantages, including high data processing efficiency, strong real …

With the prosperity of artificial intelligence, neural networks have been increasingly applied
in healthcare for a variety of tasks for medical diagnosis and disease prevention. Mobile …