Federated learning (FL) has emerged as a popular technique for distributing machine
learning across wireless edge devices. We examine FL under two salient properties of …

While federated learning (FL) eliminates the transmission of raw data over a network, it is
still vulnerable to privacy breaches from the communicated model parameters. In this work …

Federated learning has emerged as a popular technique for distributing model training
across the network edge. Its learning architecture is conventionally a star topology be-tween …

As a distributed Machine Learning (ML) technology, Federated Learning (FL) trains global
models by federating local nodes and improves the performance of each participating client …

In the last few years, Federated Learning (FL) has received extensive attention from the
research community because of its capability for privacy-preserving, collaborative learning …

The swift emergence and wide-ranging utilization of machine learning (ML) across various
industries, including healthcare, transportation, and robotics, have underscored the …

In recent years, a distributed training frame has gradually replaced the traditional cloud-
based centralized training, which is called Federated Learning (FL). It allows users of mobile …

Abstract 6G poses higher requirements on computing resources and latency. Cloud
computing, as a type of distributed computing, uses a system consisting of high-performance …

Federated learning offers a promising paradigm for training machine learning models in
distributed edge networks. This paper focuses on a critical aspect of federated learning …

The exponentially increasing demand for data, computation, low latency and reliable
communications requires the balancing and exploitation of both edge and cloud processing …