In this era of the Internet of Everything (IoE), edge computing has emerged as the critical
enabling technology to solve a series of issues caused by an increasing amount of …

Abstract Conventional Federated Learning (FL) systems inherently assume a uniform
processing capacity among clients for deployed models. However, diverse client hardware …

Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …

Neural network pruning particularly channel pruning is a widely used technique for
compressing deep learning models to enable their deployment on edge devices with limited …

Data is readily available with the growing number of smart and IoT devices. However,
application-specific data is available in small chunks and distributed across demographics …

Knowledge sharing and model personalization are essential components to tackle the non-
IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) …

We carefully evaluate a number of algorithms for learning in a federated environment, and
test their utility for a variety of image classification tasks. We consider many issues that have …

Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

Federated learning (FL) is a popular distributed machine learning paradigm involving
numerous edge devices with enhanced privacy protection. Recently, an extensive literature …

Federated learning (FL) is a distributed machine learning approach in which many clients
contribute to learning a single global model in a privacy-preserving manner on the server …