federated learning

Semantic-Aware Edge Intelligence for Cooperative Perception in UAV–V2X Mobility

This chapter presents a semantic-aware edge intelligence framework for cooperative perception in UAV–V2X mobility. It addresses the limitations of continuous raw-data sharing among vehicles, UAVs, roadside units, MEC servers, and cloud platforms under bandwidth, latency, energy, synchronization, and computing constrain...

Noise Placement, Privacy Accounting, and Structured Clipping in Client-Level Differentially Private Federated Learning An Empirical Study

This paper presents a controlled empirical study of three implementation choices in client-level differentially private federated learning: noise placement, privacy accounting, and structured clipping. Experiments are conducted under a trusted-server threat model with client-level add/remove adjacency across CIFAR-10,...

Artificial intelligence and radiomics in lung cancer: from imaging to clinical care

Artificial intelligence (AI) and radiomics have emerged as promising approaches in lung-cancer imaging by extracting quantitative features from routine medical images beyond visual assessment alone. Proof-of-concept studies span pulmonary nodule characterisation, molecular biomarker prediction, treatment-response asses...

Training Without Gathering the Data: A Historical Development Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...

Personalized vs. Federated Physiological Anomaly Detection: Comparative Analysis of Blockchain-Verified and Centralized Architectures for Military Monitoring

AI-BASED ENERGY CONSUMPTION FORECASTING AND SUSTAINABLE OPTIMIZATION: A REVIEW OF INTELLIGENT FORECASTING MODELS AND GREEN AI APPROACHES

The rapid growth of urbanization, industrialization, digital technologies, and connected devices has resulted in increasing energy demand and greater complexity in modern energy systems. Accurate energy consumption forecasting has therefore become an important component of efficient energy management, smart-grid operat...

A comprehensive study on cervical cancer diagnosis using deep learning and artificial intelligence techniques

Cervical cancer is a significant health problem across the globe, especially in nations with low or middle incomes, where access to early screening is limited and contributes to high mortality rates. Traditional diagnostic methods, including Papanicolaou smears (Pap smears) and Human Papillomavirus testing (HPV testing...

Group Distillation for Semi-supervised Federated Learning in Heterogeneous Edge Computing

GuardML: Enabling Secure and Accurate Federated Learning for Medical Diagnostics Using Hybrid Homomorphic Encryption

AI-BASED ENERGY CONSUMPTION FORECASTING AND SUSTAINABLE OPTIMIZATION: A REVIEW OF INTELLIGENT FORECASTING MODELS AND GREEN AI APPROACHES

The rapid growth of urbanization, industrialization, digital technologies, and connected devices has resulted in increasing energy demand and greater complexity in modern energy systems. Accurate energy consumption forecasting has therefore become an important component of efficient energy management, smart-grid operat...

Fairness-constrained explainable machine learning with differentially private federated training for employee attrition prediction in civil engineering and law

Employee attrition in knowledge-intensive sectors such as civil engineering and law presents compounding risks to project continuity, regulatory compliance, and organisational profitability. Despite growing adoption of machine learning (ML) in human resource (HR) analytics, no prior study has simultaneously targeted th...

Training Without Gathering the Data: A Historical Development Review of Federated Learning and Privacy-Preserving AI

This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...