Federated learning (FL) is a machine learning (ML) discipline that allows to train ML models
on distributed data without revealing raw data instances. It promises to enable ML in …

Benefiting from hardware upgrades and deep learning techniques, more and more end
devices can independently support a variety of intelligent applications. Further powered by …

Evolution of the 5G network introduces much higher QoS standards and energy saving
objectives, which requires a more refined and smoothed online control method in many …

Federated learning is renowned for its efficacy in distributed model training, ensuring that
users, called clients, retain data privacy by not disclosing their data to the central server that …

Federated Learning (FL) enables deep learning model training across edge devices and
protects user privacy by retaining raw data locally. Data heterogeneity in client distributions …

As network connectivity increasingly shapes modern vehicular applications, in-advance
knowledge of Quality-of-Service (QoS) degradation could unlock the potential for efficient …

Federated learning (FL) is a promising approach for edge/IoT-based distributed machine
learning, where both privacy and bandwidth efficiency are essential. However, as time …

Federated Learning (FL) is a privacy-preserving machine learning paradigm where a global
model is trained in-situ across a large number of distributed edge devices. These systems …