Implementing federated learning (FL) algorithms in wireless networks has garnered a wide
range of attention. However, few works have considered the impact of user mobility on the …

Federated learning (FL) enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …

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

Federated learning (FL) has emerged as a promising distributed learning paradigm that
enables a large number of mobile devices to cooperatively train a model without sharing …

Federated Learning (FL) enables the multiple participating devices to collaboratively
contribute to a global neural network model while keeping the training data locally. Unlike …

Federated learning aims to collaboratively train a machine learning model with possibly geo‐
distributed workers, which is inherently communication constrained. To achieve …

Federated learning (FL) is a challenging setting for optimization due to the heterogeneity of
the data across different clients which gives rise to the client drift phenomenon. In fact …

Abstract Cross-device Federated Learning (FL) is a distributed learning paradigm with
several challenges that differentiate it from traditional distributed learning: variability in the …

Federated Learning is a distributed learning paradigm with two key challenges that
differentiate it from traditional distributed optimization:(1) significant variability in terms of the …