federated learning

Additive Manufacturing and the Rise of Digital Inventory

The chapter discusses how additive manufacturing (AM) is helping speed the shift of the physical stockpile to a digital inventory on-demand, file-based production as close as possible to the point of need. This is transforming the coming times, reducing logistics costs, enhancing customization, and servicing of product...

AI-Driven Cyber Threat Prediction Framework for Smart Industrial Systems Using Federated Learning

Green and Responsible AI: Emerging Trends, Challenges, and Future Directions in Sustainable Computing

This review examines Green AI as an emerging paradigm for developing energy-efficient machine learning systems and explores its intersection with the principles of responsible AI, providing a roadmap for researchers and practitioners seeking to develop AI systems that balance computational performance with environmenta...

A Survey on Resource Management Solutions, Challenges, and Emerging Opportunities in Fog Computing

Fog computing positions computation and storage resources in close proximity to data sources, promisingmillisecond-scale responsiveness and data handling that the distant cloud cannot deliver. Over the pastdecade, researchers have proposed a constellation of mechanisms such as virtualisation, latency-awareservice place...

Deep Learning for Type 2 Diabetes Prediction: A Benchmarking Study Using the PIMA Indians Diabetes Dataset

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with a growing global burden, making early and accurate risk prediction a public-health priority. This study develops and evaluates a deep neural network (DNN) for T2DM prediction using the PIMA Indian Diabetes dataset (768 records and 8 clinical features)...

Deep Learning for Type 2 Diabetes Prediction: A Benchmarking Study Using the PIMA Indians Diabetes Dataset

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder with a growing global burden, making early and accurate risk prediction a public-health priority. This study develops and evaluates a deep neural network (DNN) for T2DM prediction using the PIMA Indian Diabetes dataset (768 records and 8 clinical features)...

Machine Learning Applications for IoT Intrusion Detection: Network Dependencies, Dataset Limitations, and Regulatory Compliance—A Systematic Review

Generative adversarial networks (GANs) dominate dataset augmentation work, graph-based communication analysis addresses dependency modelling, and methods based on transformers or federated learning emerge from 2023 onward.

Challenges of Cybersecurity in Intelligent Health Systems

The evolution of healthcare into a data-centric, intelligent, and connected field has resulted in better diagnostic precision, targeted treatments, remote monitoring, and operational effectiveness. But the evolution also makes sensitive medical information and vital health operations vulnerable to cybersecurity threats...

A Severity-Calibrated Adversarial Benchmark for Federated Learning: From Label Corruption to Structured Model-Update Injection

A REVIEW ON ARTIFICIAL INTELLIGENCE AND DIGITAL HEALTH IN VIRAL HEPATITIS CARE: CURRENT LANDSCAPE AND FUTURE HORIZONS

Viral hepatitis remains one of the most significant global public health challenges, affecting more than 300 million individuals worldwide and causing approximately 1.3 million deaths annually due to cirrhosis, liver failure, and hepatocellular carcinoma (HCC). Despite remarkable advances in vaccination strategies, hig...

Code for: Detection-Aware Content-Level Differential Privacy for Lattice-Trapdoor Traceable Federated Prototype Learning

This repository contains the runnable implementation and experiment scripts accompanying the paper "Detection-Aware Content-Level Differential Privacy for Lattice-Trapdoor Traceable Federated Prototype Learning". It implements a lattice-trapdoor identity-tracing mechanism for federated prototype learning (FedLHT) combi...

A Survey on Resource Management Solutions, Challenges, and Emerging Opportunities in Fog Computing

Fog computing positions computation and storage resources in close proximity to data sources, promisingmillisecond-scale responsiveness and data handling that the distant cloud cannot deliver. Over the pastdecade, researchers have proposed a constellation of mechanisms such as virtualisation, latency-awareservice place...