Federated Learning (FL) is a distributed model training framework that allows multiple
clients to collaborate on training a global model without disclosing their local data in edge …

This paper tackles the problem of training Federated Learning (FL) algorithms over real-
world wireless networks with packet losses. Lossy communication channels between the …

Federated learning is a powerful paradigm for large-scale machine learning, but it faces
significant challenges due to unreliable network connections, slow communication, and …

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 …

Federated learning is a distributed machine learning technique that addresses the
challenges of traditional centralized machine learning, such as high computational …

In this work, we investigate the challenging problem of on-demand federated learning (FL)
over heterogeneous edge devices with diverse resource constraints. We propose a cost …

Abstract In Federated Learning (FL), a common approach for aggregating local solutions
across clients is periodic full model averaging. It is, however, known that different layers of …

Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …

Federated learning has attracted increasing attention with the emergence of distributed data.
While extensive federated learning algorithms have been proposed for the non-convex …

Federated Learning (FL) is a variant of distributed learning where edge devices collaborate
to learn a model without sharing their data with the central server or each other. We refer to …