With the proliferation of mobile computing and Internet of Things (IoT), massive mobile and
IoT devices are connected to the Internet. These devices are generating a huge amount of …

Recent advances in artificial intelligence have driven increasing intelligent applications at
the network edge, such as smart home, smart factory, and smart city. To deploy …

Federated Learning (FL) is viewed as a promising manner of distributed machine learning,
because it leverages the rich local datasets of various participants while preserving their …

With the increased penetration and proliferation of Internet of Things (IoT) devices, there is a
growing trend towards distributing the power of deep learning (DL) across edge devices …

Deep neural networks (DNNs) have been increasingly deployed on and integrated with
edge devices, such as mobile phones, drones, robots and wearables. To run DNN inference …

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …

As a compelling collaborative machine learning framework in the big data era, federated
learning allows multiple participants to jointly train a model without revealing their private …

The demand for intelligent services at the network edge has introduced several research
challenges. One is the need for a machine learning architecture that achieves …

Edge intelligence is an emerging paradigm for real-time training and inference at the
wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) …

With the wave of the Internet of Things (IoT), a vast number of IoT devices are connected to
wireless networks. To better support the Quality of Service of IoT devices with constrained …