In recent years, deep learning algorithms have rapidly revolutionized artificial intelligence,
particularly machine learning, enabling researchers and practitioners to extend previously …

Artificial Intelligence (Al) models are being produced and used to solve a variety of current
and future business and technical problems. Therefore, AI model engineering processes …

In cross-silo federated learning (FL), the data among clients are usually statistically
heterogeneous (aka not independent and identically distributed, non-IID) due to diversified …

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to
accelerate the response of IoT services by deploying edge intelligence near IoT devices …

Federated Learning (FL) has emerged as a transformative paradigm in machine learning,
enabling decentralized model training across multiple devices while preserving data …

Federated learning (FL) is a new and promising paradigm that allows devices to learn
without sharing data with the centralized server. It is often built on decentralized data where …

This paper applies federated reinforcement learning (FRL) in cellular vehicle-to-everything
(C-V2X) communication to enable vehicles to learn communication parameters in …

With the wide adoption of cryptocurrency, blockchain technologies have become the
foundation of such digital currencies. However, this adoption has been accompanied by a …

Federated learning (FL) has recently gained popularity as a distributed machine learning
approach that protects privacy. However, this concept has not yet been extensively adopted …

The advancement of digitization and automation in Low Altitude Intelligent Networking
(LAIN) is constrained by limited computational resources and the absence of a dedicated …