In federated learning (FL), reducing the communication … devices share the training parameters
over wireless links. With such … balance between the overall learning performance and their …

Federated learning (FL) is a promising solution to privacy preservation for data-driven deep
learning … aerial vehicle (UAV)-assisted wireless networks is still challenging due to limited …

… In this paper, we consider a network of wireless devices … 1, we consider a decentralized
federated learning model, in … shared machine learning model via wireless D2D communications …

… The key objective of our work is to improve the convergence time of federated learning …
-hop wireless networks. To tame the network latency and to implement reinforcement learning …

… Discussions In this subsection, we discuss the impacts of the depth l of neural network and
local training round τ on the federated learning performance based on Lemma 2. The optimal …

… machine learning architecture. Federated learning (FL) is a distributed machine learning
architecture that allows different devices to train a machine learning model collaboratively …

… learning algorithms in wireless IoT networks. The traditional parameter server architecture for
federated learn… propose a decentralized wireless federated learning algorithm called DWFL…

… and wireless resource allocation in wireless HFL under both IID and non-IID cases, respectively.
First, we analyze the learning performance, ie, model error and learning … on the learning …

… tion approach for on-device federated learning via over-the-air computation. To improve
the performance and the convergence rate for federated learning, we propose a joint device …

… devices with intelligent adaptive learning and reduce expensive traffic in SatCom, we
propose federated learning (FL) in LEO-based satellite communication networks. We first review …