federated learning

HuddsTrafficFL: A Benchmark Dataset for Byzantine-Robust Federated Learning in Urban Traffic Systems

HuddsTrafficFL is a benchmark dataset for Byzantine-robust federated learning research in urban traffic systems. Built from a city-scale SUMO/TraCI simulation of Huddersfield's road network using real OpenStreetMap data and validated against UK Department for Transport traffic counts for Kirklees borough. The dataset i...

Federated learning guided by model performance: a new aggregation strategy

ZeroTrust-FL-Sim

A reproducible zero-trust federated-learning security and resilience simulation platform covering non-IID data, malicious clients, Byzantine-robust aggregation, differential privacy, CKKS aggregation, post-quantum transport, acceleration, chaos engineering, and observability.

Quantum Federated Digital Twin Framework with Explainable Multi-Agent Intelligence for Autonomous Engineering Management

Synergizing artificial intelligence/machine learning and digital twin computing

Internet of Things and Machine Learning for Sustainable Development: A Systematic Review and Conceptual Framework

The integration of the Internet of Things (IoT) and Machine Learning (ML) has emerged as a transformative technological paradigm for addressing global sustainability challenges through intelligent sensing, real-time data analytics, and autonomous decision-making. Although numerous studies have explored IoT-ML applicati...

Deep Learning for Intelligent Waste Management and Real-Time Organic and Recyclable Waste Classification for Smart Cities

Growing waste production and rapid urbanization pose serious logistical and environmental challenges. Reducing the burden on landfills and promoting the circular economy rely on effective separation of recyclable and organic waste. In this chapter, a deep learning based real-time classification framework that uses conv...

HuddsTrafficFL: A Benchmark Dataset for Byzantine-Robust Federated Learning in Urban Traffic Systems

HuddsTrafficFL is a benchmark dataset for Byzantine-robust federated learning research in urban traffic systems. Built from a city-scale SUMO/TraCI simulation of Huddersfield's road network using real OpenStreetMap data and validated against UK Department for Transport traffic counts for Kirklees borough. The dataset i...

Ten key considerations for developing interpretable risk prediction models

Artificial intelligence (AI) and machine learning (ML) have rapidly transformed clinical risk prediction by enabling high-dimensional data analysis, automated pattern recognition, and individualized risk estimation [1]. Compared with conventional regression models, ML algorithms can automatically learn nonlinear relati...

SimCortex v2: Pre-trained Models and Reproducibility Files

SimCortex v2 is a deep-learning framework for simultaneous reconstruction of left and right white-matter and pial cortical surfaces from T1-weighted MRI. This Zenodo record provides the finalized pretrained segmentation and deformation model weights, frozen training/inference/evaluation configurations, the HCP-YA/OASIS...

ZeroTrust-FL-Sim

A reproducible zero-trust federated-learning security and resilience simulation platform covering non-IID data, malicious clients, Byzantine-robust aggregation, differential privacy, CKKS aggregation, post-quantum transport, acceleration, chaos engineering, and observability.

A Federated Multi-Agent Communication Framework for Autonomous Cyber Defense in Edge–Cloud IoT Environments

The FMAD framework integrates multiple collaborative agents for secure communication, federated learning, and coordinated cyber defense in edge–cloud IoT environments and provides a promising foundation for intelligent agent communication and scalable autonomous cyber defense in real-world edge–cloud IoT environments.