Federated learning (FL) is a decentralized algorithm that can train a globally shared model
without the requirement to send the raw data to a centralized server by user equipments …

Federated learning (FL) can train a global model from clients' local data set, which can make
full use of the computing resources of clients and performs more extensive and efficient …

Owing to the increasing need for massive data analysis and model training at the network
edge, as well as the rising concerns about the data privacy, a new distributed training …

Federated learning (FL) can train a global model from clients' local dataset, which can make
full use of the computing resources of clients and performs more extensive and efficient …

Federated Learning (FL) is an emerging technique to enhance edge intelligence, where
mobile devices train machine learning models collaboratively with their local data. Limited …

Federated Learning (FL) is a collaborative machine learning (ML) framework that combines
on-device training and server-based aggregation to train a common ML model among …

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data
distributions, computing powers, and channel conditions of participating devices. This paper …

Federated learning (FL) enables clients to collaboratively learn a shared task while keeping
data privacy, which can be adopted at the edge of wireless networks to improve edge …

In this work, we propose a novel joint client scheduling and resource block (RB) allocation
policy to minimize the loss of accuracy in federated learning (FL) over wireless compared to …

Federated Learning (FL) is a promising decentralized machine learning technique, which
can be efficiently used to reduce the latency and deal with the data privacy in the next 6th …