The fog paradigm extends the cloud capabilities at the edge of the network. Fog computing-
based real-time applications (Online gaming, 5G, Healthcare 4.0, Industrial IoT, autonomous …

Edge computing is a viable approach to improve service delivery and performance
parameters by extending the cloud with resources placed closer to a given service …

Federated learning has been explored as a promising solution for training machine learning
models at the network edge, without sharing private user data. With limited resources at the …

As cloud computing becomes the dominant mechanism for delivery of electronic services,
significant recent effort has focused on certifying cloud services to ensure their compliance …

Edge computing has emerged as a critical infrastructure for deploying AI-driven
microservices, particularly for applications requiring low-latency and high-performance, such …

Deploying end‐to‐end ML applications on edge resources becomes a viable solution to
achieve performance and data regulations. With the microservice architecture, these …

The increasing demand for low-latency high-performance applications motivates the
development of network and compute infrastructure. As an emerging paradigm, edge …

The Internet of Things fosters an emerging class of analytics that connects sensors, vehicles,
industries, and consumers through the internet to enable scientific and industrial …

A task offloading model based on deep reinforcement learning and user experience degree
is proposed. Firstly, after users generate blockchain tasks, Proof of Work (PoW) consensus …

Federated learning has been explored as a promising solution for training machine learning
models at the edge, where edge devices collaborate to train models without sharing private …