Federated learning (FL) has emerged as a promising alternative to centralized machine
learning for exploiting large amounts of data generated by networks while ensuring data …

Federated Learning (FL) enables the distributed machine learning (ML) without violating the
privacy of local users. In the scenario wireless FL, it is challenging for some local clients to …

Unmanned aerial vehicles (UAVs) mobility enables flexible and customized federated
learning (FL) at the network edge. However, the underlying uncertainties in the aerial …

Federated learning (FL) is promising in enabling large-scale model training by massive
devices without exposing their local datasets. However, due to limited wireless resources …

Federated learning (FL) allows UAVs to collaboratively train a globally shared machine
learning model while locally preserving their private data. Recently, the FL in edge-aided …

The explosive growth of smart devices (eg, mobile phones, vehicles, drones) with sensing,
communication, and computation capabilities gives rise to an unprecedented amount of …

There is an increasing interest in a fast-growing machine learning technique called
Federated Learning (FL), in which the model training is distributed over mobile user …

Federated Learning (FL) is a machine learning approach that enables the creation of shared
models for powerful applications while allowing data to remain on devices. This approach …

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

With huge amounts of data explosively increasing on the mobile edge, over-the-air
federated learning (OA-FL) emerges as a promising technique to reduce communication …