federated learning

Fedadasparse: Communication-Efficient Personalized Federated Learning Via Similarity-Based Client Clustering And Adaptive Gradient Sparsification For Non-IID Edge Data

Federated learning (FL) enables collaborative model training across distributed clients without centralizing raw data, but its practical deployment on resource-constrained edge devices is hindered by two persistent challenges: statistical heterogeneity (non-independent and identically distributed, non-IID, client data)...

MOMENTUM-OPTIMIZED FEDERATED DEEP LEARNING FOR INTRUSION DETECTION IN INDUSTRIAL CONTROL SYSTEMS: AN ARCHITECTURAL EVALUATION

Industrial Control Systems (ICS) are critical infrastructures designed to monitor and control industrial processes through field elements such as sensors, actuators, and machinery.With the increasing integration of IoT devices into ICS networks, the landscape of cybersecurity threats has expanded significantly, necessi...

Secure Telemedicine in Medical Tourism: A Federated Learning Architecture for Privacy-Preserving Cross-Border Healthcare

Systematic Review of Predictive Intelligence Models in ProductCentric 6G Network Planning and Optimization

Predictive intelligence models serve as the backbone of next-generation 6G network planning and optimization and offer promising directions for delivering intelligent, resilient, and user-aware telecom services, according to a systematic review of current research.

State‐of‐Charge Estimation of Second‐Life Batteries Using the Federated Learning

ABSTRACT The advancement in automobile electrification has been causing concern about retired batteries. Since the battery cannot store and deliver energy for a high‐speed vehicle when its state of health (SOH) is below 80%, this battery must have another destination. A second application for energy storage from solar...

Hi-FL: Hierarchical Federated Learning Adaptation of Vision-Language Models for Multi-institutional Surgical Phase Recognition

TyMill/FedGridSim: v1.2.0

FedGridSim v1.2.0 — Reviewer Revision Release Overview FedGridSim v1.2.0 is the reproducibility release accompanying the revised manuscript: "Federated Learning for Resilient Smart-Grid Monitoring: A Physics-Based Simulation Framework for Distributed Energy Networks." The release consolidates the simulation, federated-...

Alzheimer's detection using quanvolutional neural networks with federated training

Alzheimer's disease detection from MRI data is challenging because of subtle anatomical patterns, high-dimensional image data, and privacy constraints associated with centralized medical-data training. We propose a hybrid quantum–classical federated learning framework using a frozen four-qubit quanvolutional front-end....

A Conceptual Framework for Intelligent IoT and AI-Driven Neurocognitive Healthcare Assistive Systems

Intelligent Internet of Things (IoT) and assistive technologies are revolutionizing neurocognitive healthcare, with the ability to continuously monitor, use early diagnostics, assess disease progression and deliver personalized interventions. In this chapter, a conceptual framework concerning the integration of wearabl...

IoT-Enabled Wearable Systems and AI-Driven Early Detection of Neurocognitive Decline

More than 55 million individuals suffer from neurocognitive disorders — such as Alzheimer's disease, Parkinson's disease, and dementia — and the number of people suffering from neurocognitive disorders is projected to increase to over 150 million by 2050 as populations around the world grow older. Most people who have...

Artificial Intelligence, Machine Learning, and IoT in Alzheimer's Disease Prediction

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that requires accurate and early diagnosis for effective management. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have significantly improved AD prediction through the analysis of complex biomedical an...

A simulation study of differentially private federated learning with zero-trust policy screening for medical IoT devices

Artificial intelligence is increasingly used in connected medical-device systems for physiological-signal classification and continuous monitoring. This paper presents a simulation study of the learning and policy layers of a zero-trust federated-learning framework for Internet of Medical Things (IoMT) devices. The imp...