To exploit massive amounts of data generated at mobile edge networks, federated learning
(FL) has been proposed as an attractive substitute for centralized machine learning (ML). By …

Intelligent communication is gradually becoming a mainstream direction. As a major branch
of machine learning, deep learning (DL) has been applied in physical layer communications …

Lots of hopes have been placed on machine learning (ML) as a key enabler of future
wireless networks. By taking advantage of large volumes of data, ML is expected to deal with …

This paper proposes a cooperative mechanism for mitigating the performance degradation
due to non-independent and-identically-distributed (non-IID) data in collaborative machine …

Traditional communication systems design is dominated by methods that are based on
statistical models. These statistical-model-based algorithms, which we refer to henceforth as …

Federated learning has gained popularity as a means of training models distributed across
the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) …

We consider federated edge learning (FEEL), where mobile users (MUs) collaboratively
learn a global model by sharing local updates on the model parameters rather than their …

Federated learning (FL) enables multiple clients to collaborate on a common learning task
via only exchanging model updates. With the progressive improvements in deep learning …

Graph neural network (GNN) is an efficient neural network model for graph data and is
widely used in different fields, including wireless communications. Different from other …

The popularity of mobile devices results in the availability of enormous data and
computational resources at the network edge. To leverage the data and resources, a new …