… Federated learning (FL) is an emerging paradigm that enables multiple devices to collaborate
in training machine learning (ML) … In this article, we propose a communication-efficient FL …

… environment, we conclude that a communicationefficient distributed training algorithm for
federated learning needs to fulfil the following requirements. (R1): It should compress both …

… We address the problem of Federated Learning (FL) where users are distributed and partitioned
into clusters. This setup captures settings where different groups of users have their own …

… build a shared learning model with training their collected data locally [6]–[15]. One of the
most promising distributed learning algorithms is the emerging federated learning (FL) …

… Federated Learning. For simplicity, we consider synchronized algorithms for Federated Learning
… We conducted experiments using Federated Learning to train deep neural networks for …

… Federated learning (FL) is a distributed learning paradigm that enables a large number of
devices to collaboratively learn … Tassiulas, “Model pruning enables efficient federated learning …

… This article first illustrates the typical use cases of federated learning in mobile edge …
approaches in federated learning. The resource-efficient techniques for federated learning are …

… Federated learning is a powerful distributed learning scheme that allows numerous edge …
first progressive training framework for efficient and effective federated learning. It inherently …

… dictors is trained via federated learning. This method allows … with on-device data through
federated learning. Motivated by … -efficient ensemble algorithms for federated learning, where …

… To address this problem, federated learning (FL) has emerged … we perform FL efficiently so
that a model is trained within a … generate a small model for efficient computation. Furthermore, …