In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

Split learning (SL) is a collaborative learning framework, which can train an artificial
intelligence (AI) model between a device and an edge server by splitting the AI model into a …

In this paper, we present a three-layer (ie, device, field, and factory layers) deterministic
federated learning (FL) framework, named DetFed, which accelerates collaborative learning …

Over-the-air federated edge learning (Air-FEEL) has emerged as a communication-efficient
solution to enable distributed machine learning over edge devices by using their data locally …

Over-the-air computation (AirComp) has emerged as a new analog power-domain non-
orthogonal multiple access (NOMA) technique for low-latency model/gradient-updates …

Federated learning (FL) over mobile devices has fostered numerous intriguing
applications/services, many of which are delay-sensitive. In this paper, we propose a service …

Training a machine learning model with federated edge learning (FEEL) is typically time
consuming due to the constrained computation power of edge devices and the limited …

Federated Learning (FL) is an intriguing distributed machine learning approach due to its
privacy-preserving characteristics. To balance the trade-off between energy and execution …

This paper considers improving wireless communication and computation efficiency in
federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge …