Federated learning (FL) is a popular privacy-preserving distributed training scheme, where
multiple devices collaborate to train machine learning models by uploading local model …

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

Federated edge learning (FEEL) has emerged as an effective approach to reduce the large
communication latency in Cloud-based machine learning solutions, while preserving data …

Federated learning (FL) is a promising learning paradigm that can tackle the increasingly
prominent isolated data islands problem while keeping users' data locally with privacy and …

To satisfy the expected plethora of computation-heavy applications, federated edge learning
(FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-latency …

Federated learning (FL) is an emerging technique that enables privacy-preserving
distributed learning. Most related works focus on centralized FL, which leverages the …

Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial
intelligence paradigm, where over-the-air computation enables spectral-efficient model …

Federated learning (FL) as a promising edge-learning framework can effectively address the
latency and privacy issues by featuring distributed learning at the devices and model …

Federated edge learning (FEEL) is a popular distributed learning framework for privacy-
preserving at the edge, in which densely distributed edge devices periodically exchange …

Client and Internet of Things devices are increasingly equipped with the ability to sense,
process, and communicate data with high efficiency. This is resulting in a major shift in …