Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

In the horizontal federated learning setting, multiple clients jointly train a model under the
coordination of the central server, while the training data is kept on the client to ensure …

The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while keeping privacy of training data on clients. Recently, many …

Federated learning (FL) has recently emerged as a striking framework for allowing machine
and deep learning models with thousands of participants to have distributed training to …

Decentralized Federated Learning (DeFL) plays a critical role in improving effectiveness of
training and has been proved to give great scope to the development of edge computing …

Federated learning (FL) enables the collaborative training of machine learning models
without sharing training data. Traditional FL heavily relies on a trusted centralized server …

Abstract The Tactile Internet of Things (TIoT) represents a special class of the Internet of
Things (IoT) that has opened the door for a new generation of agile, highly dynamic …

TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-
power devices to process their own data. The lack of resources restricts the aforementioned …

Traditional machine learning relies on a centralized data pipeline for model training in
various applications; however, data are inherently fragmented. Such a decentralized nature …

Federated learning (FL) provides a novel framework to collaboratively train a shared model
in a distribution fashion by virtue of a central server. However, FL is inappropriate for a …