Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

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

Federated learning (FL) over resource-constrained wireless networks has recently attracted
much attention. However, most existing studies consider one FL task in single-cell wireless …

To leverage massive distributed data and computation resources, machine learning in the
network edge is considered to be a promising technique, especially for large-scale model …

While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably
degrades the utility (ie, accuracy) of FL due to model perturbations caused by DP noise …

Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is
capable of generating personalized models for heterogenous clients. Combined with a meta …

Task-oriented communications, mostly using learning-based joint source-channel coding
(JSCC), aim to design a communication-efficient edge inference system by transmitting task …

Unmanned aerial vehicles (UAVs) have been widely used to perform search and tracking
tasks in military and civil fields. To perform these tasks autonomously, a swarm of multiple …

Federated Learning (FL) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …

Federated learning is a popular distributed machine learning paradigm that enables
collaborative model training at multiple entities via exchanging intermediate learning results …