Federated edge learning (FEEL) is a rapidly growing distributed learning technique for next-
generation wireless edge systems. Smart systems across various application domains face …

Federated learning (FL) involves training machine learning models over distributed edge
nodes (ie, workers) while facing three critical challenges, edge heterogeneity, Non-IID data …

Efficient collaboration between collaborative machine learning and wireless communication
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …

Federated Edge Learning (FEEL) involves the collaborative training of machine learning
models among edge devices, with the orchestration of a server in a wireless edge network …

Federated Learning (FL) is a distributed machine learning technique, where each device
contributes to the learning model by independently computing the gradient based on its …

Federated Learning (FL) has been proposed as an appealing approach to handle data
privacy issue of mobile devices compared to conventional machine learning at the remote …

Federated learning (FL) has received significant attention in recent years for its advantages
in efficient training of machine learning models across distributed clients without disclosing …

Federated Learning (FL) is a state-of-the-art paradigm used in Edge Computing (EC). It
enables distributed learning to train on cross-device data, achieving efficient performance …

Hierarchical federated edge learning (H-FEEL) has been recently proposed to enhance the
federated learning model. Such a system generally consists of three entities, ie, the server …

Federated learning (FL) has been widely adopted to train machine learning models over
massive data in edge computing. However, machine learning faces critical challenges, eg …