federated learning

Resilient 6G Optical-Wireless Convergence with Federated, Hierarchical Reinforcement Learning, and Triple Connectivity

The sixth generation (6G) mobile network demands exceptional resilience to support mission-critical applications such as remote surgery, autonomous driving, and industrial automation. Conventional architectures typically separate the wireless access and optical transport domains, which limits coordination and increases...

A Hybrid Model for Minimizing Malware Threats in Cloud Computing Using Generative Adversarial Networks (GANs) and Federated Learning (FL)

The objective of this study is to develop a hybrid model for minimizing malware threats in cloud computing model using Generative Adversary Networks (GANs) and Federated Learning (FL), to improve detection accuracy and reduce false positives using the hybrid model. The project was motivated by the prevailing challenges...

ADVANCED COMPUTING TECHNOLOGIES: IoT,BLOCKCHAIN, AI, AND MACHINE LEARNING

The rapid convergence of the Internet of Things (IoT), Blockchain, Artificial Intelligence (AI), and Machine Learning (ML) is fundamentally reshaping the landscape of modern digital infrastructure. While each of these paradigms has individually driven massive technological leaps, their true transformative power lies in...

Multiscale computational modeling and machine learning for energy aware safety monitoring in open pit coal mines

The behaviors of open-pit coal mines are very dynamic and nonlinear at various levels, including the operations of equipment, as well as large-scale geotechnical and environmental conditions. The combination of multiscale computational modeling with machine learning is a promising avenue to improving safety monitoring...

Blockchain-based distributed federated learning using proof of accuracy consensus

EPACE-FL: Efficient Privacy-Preserving And Communication in Edge Computing of Federated Learning

An Adaptive Curriculum Event-Aware Federated Averaging Framework for Resource Demand Prediction in 5G Non-Standalone Networks

The dynamic and heterogeneous 5G traffic complicates short-term resource-demand prediction. While federated learning (FL) enables privacy-preserving training by keeping raw data local. However, FedAvg and FedProx do not explicitly account for client traffic heterogeneity or sudden demand variations. To address these li...

FedMetaOpt: a comprehensive survey of metaheuristic optimization for federated learning in client selection, resource allocation, and model aggregation

Edge AI and Federated Learning for Privacy-Preserving IoT Systems: System Architectures, Non-IID Optimization, and Security Bounds

Abstract—The proliferation of Internet of Things (IoT) devices has created an unprecedented surge in decentralized telemetry, sensor data, and localized computational workloads. Conventional cloud-centric machine learning architectures require centralized pooling of raw edge data, which induces prohibitive wide-area ne...

FedQDFU: Non-IID Federated Quantum Learning for Diabetic Foot Ulcer Classification from Deep Image Features

Diabetic foot ulcer (DFU) classification increasingly relies on learning frameworks that exploit medical image representations without centralizing raw data. This study presents FedQDFU, a feature-based federated quantum learning benchmark evaluating a compact variational quantum classifier (VQC) on deep features from...

QLFL: quantum-enabled lightweight federated learning-empowered threat detection model for Internet of Medical Things based healthcare

The integration of Internet of Medical Things (IoMT) has significantly reshaped the landscape of modern healthcare, facilitating seamless communication between connected IoMT devices. Despite these advancements, the growing adoption of healthcare technologies has raised critical cybersecurity and data privacy concerns....

Symbiotect Network: A Federated Artificial Immune System for Advanced AI

This concept paper proposes a Federated Artificial Immune System for advanced AI (FAIIS), an architecture for detecting, containing, learning from and sharing defenses against unauthorized AI behavior after a model begins to cross its intended boundary. The design borrows its structure from the human immune system. San...