Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Abstract Personalized Federated Learning (pFL), which utilizes and deploys distinct local
models, has gained increasing attention in recent years due to its success in handling the …

Distributed machine learning (ML) can bring more computational resources to bear than
single-machine learning, thus enabling reductions in training time. Distributed learning …

Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …

Federated learning (FL) becomes a promising machine learning paradigm. The impact of
heterogeneous hardware specifications and dynamic states on the FL process has not yet …

Recently, in healthcare organizations, real-time data have been collected from connected or
implantable sensors, layered protocol stacks, lightweight communication frameworks, and …

With rapid advancement of Internet of Things (IoT) and social networking applications
generating large amounts of data at or close to the network edge, Mobile Edge Computing …

Federated learning (FL) training global machine learning models over distributed edge
devices has attracted sustained attentions. However, the heterogeneity of client data …

Federated Edge Learning (FEL) enables a massive number of edge devices (eg smart
phones) to train machine learning models collaboratively. Due to the inherent unreliability of …

Current advances in the Internet of Things (IoT) and Cloud involve the presence of an
additional layer between them acting as mediator for data transfer and processing in close …