federated learning

VDFedDiff: a novel versatile defense scheme against adversarial attacks in federated diffusion models

Federated learning (FL) enables multiple clients to collaboratively train machine learning models without sharing their raw data, making it a promising paradigm for privacy-sensitive generative applications. However, diffusion models trained in federated environments remain vulnerable to backdoor, evasion, and model-po...

Differential Privacy-Assisted Lightweight Explainable Multi-Dimensional Learning for Early Gestational Diabetes Prediction in IoMT Applications

Adaptive and Fault-Tolerant Scheduling Framework for Scalable and High-Performance Distributed Systems with Dynamic and Heterogeneous Workloads

High-performance distributed systems (HPDS) are increasingly challenged by dynamic workloads, heterogeneous resources, and susceptibility to faults, which degrade scheduling efficiency and resilience. Traditional heuristic and learning-based scheduling approaches, on the other hand, tend to be poorly adaptable and ha...

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...