federated learning

Blockchain for collaborative artificial intelligence/machine learning in digital twin computing

Significance-Aware Federated Reinforcement Learning for AoI Optimization of Vehicular Sensing in UAV-Assisted Edge Networks

The effectiveness of CHFAC for freshness-aware vehicular sensing in dynamic UAV-assisted edge environments is demonstrated and the proposed collaborative heterogeneous federated actor–critic framework is proposed.

Deep learning in gait abnormality detection: Principles and illustrations

shailinsta/ZT-TRUSTFED: v1.0.2

ZT-TrustFed: A Trust-Aware Federated Intrusion Detection Framework for Heterogeneous and Non-IID Security Data ZT-TrustFed is a trust-based federated learning system for intrusion detection that integrates: FedProx regularization Dynamic trust-based weighted aggregation (T-Krum) Gaussian differential privacy Single-ste...

Interpretable Federated Deep Learning with Double-Structured Orthogonal Matching Pursuit for Downlink Channel Estimation in Multi-RIS mm-Wave Massive-MIMO OFDM Systems

Introduction to artificial intelligence and machine learning

Artificial Intelligence-Based Alzheimer’s Disease Classification Using Brain MRI: A Review of Machine Learning, Deep Learning and Emerging Intelligent Approaches

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects memory, reasoning, language, and other cognitive functions. Early identification is important because structural brain changes may occur before severe clinical symptoms become evident. Magnetic Resonance Imaging (MRI) provides detailed an...

Optimal estimation in private distributed functional data analysis

We systematically investigate the preservation of differential privacy in functional data analysis, beginning with functional mean estimation and extending to varying coefficient model estimation. Our work introduces a distributed learning framework involving multiple servers, each responsible for collecting several sp...

Optimal estimation in private distributed functional data analysis

We systematically investigate the preservation of differential privacy in functional data analysis, beginning with functional mean estimation and extending to varying coefficient model estimation. Our work introduces a distributed learning framework involving multiple servers, each responsible for collecting several sp...

Federated learning for secure and privacy-preserving edge AI in smart cities’ wastewater management systems

The rapid evolution of smart cities has significantly transformed wastewater management systems through the integration of real-time monitoring, intelligent control, and data-driven decision-making. However, conventional centralized artificial intelligence (AI) frameworks face critical limitations, including data priva...

PrivFL-SMPC: Privacy-aware federated learning using secure multi-party computation for collaborative diabetes diagnosis in the cloud

Collaborative analytics across multiple healthcare institutions has become increasingly important for developing accurate predictive models while preserving patient privacy. Conventional centralized machine learning approaches require data sharing, creating significant privacy, security, and regulatory challenges. To a...

AI for Surface Water Quality Prediction: A Comprehensive Review

Artificial intelligence for surface water quality prediction has evolved rapidly from classical regression and ensemble learning toward deep sequence, graph-based, physics-informed, and representation-learning approaches, while a largely disconnected systems literature has matured around digital twins, edge intelligenc...