Federated learning has received significant attention as a potential solution for distributing
machine learning (ML) model training through edge networks. This work addresses an …

Federated Learning (FL) is a distributed learning paradigm that can coordinate
heterogeneous edge devices to perform model training without sharing private raw data …

Federated Learning (FL) is a distributed machine learning framework in communication
network systems. However, the systems' Non-Independent and Identically Distributed (Non …

The federated learning (FL) framework trains a machine learning model using decentralized
data stored at edge client devices by periodically aggregating locally trained models …

The enormous amount of data produced by mobile and IoT devices has motivated the
development of federated learning (FL), a framework allowing such devices (or clients) to …

Federated learning (FL) enables distributed model training from local data collected by
users. In distributed systems with constrained resources and potentially high dynamics, eg …

Federated Averaging (FedAvg) and its variants are prevalent optimization algorithms
adopted in Federated Learning (FL) as they show good model convergence. However, such …

Federated Learning is an emerging direction in distributed machine learning that en-ables
jointly training a model without sharing the data. Since the data is distributed across many …

We propose cooperative edge-assisted dynamic federated learning (CE-FL). CE-FL
introduces a distributed machine learning (ML) architecture, where data collection is carried …

Major bottlenecks of large-scale Federated Learning (FL) networks are the high costs for
communication and computation. This is due to the fact that most of current FL frameworks …