Federated Learning (FL) is a distributed model training paradigm that preserves clients' data
privacy. FL hyper-parameters significantly affect the training overheads in terms of time …

Federated learning is a new machine learning paradigm. The goal is to build a machine
learning model from the data sets distributed on multiple devices–so-called an isolated data …

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an
added advantage of data privacy. With the growing interest in having collaboration among …

Federated Learning (FL) has been considered as an appealing framework to tackle data
privacy issues of mobile devices compared to conventional Machine Learning (ML). Using …

With the increasing computing power of edge devices, Federated Learning (FL) emerges to
enable model training without privacy concerns. The majority of existing studies assume the …

As an emerging paradigm, federated learning (FL) trains a shared global model by multi-
party collaboration without leaking privacy since no private data transmission between the …

Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-
the-cloud setting to distributed training over edge devices in order to strengthen data …

Standard federated learning (FL) algorithms typically require multiple rounds of
communication between the server and the clients, which has several drawbacks including …

Conventional systems are usually constrained to store data in a centralized location. This
restriction has either precluded sensitive data from being shared or put its privacy on the …

Recently, federated learning (FL) has emerged as a promising distributed machine learning
(ML) technology, owing to the advancing computational and sensing capacities of end-user …