federated learning

The Evolving Security of the Internet of Vehicles: A Survey from Classical Machine Learning to Large Language Models, Adversarial Robustness, and Explainable AI

The Internet of Vehicles (IoV) connects vehicles, roadside infrastructure, and cloud or edge platforms to support safer and more efficient transportation. This connectivity also exposes vehicular networks to message spoofing, denial-of-service, and man-in-the-middle attacks, and to threats that target the machine-learn...

Divide-and-Conquer Decomposition Strategies for Real-Time Artificial Intelligence Decision Systems: A Comparative Survey

Abstract—Real-time artificial intelligence (AI) decision systems must resolve complex inputs — a video frame, a network flow, a multi-agent traffic scene — within a bounded time budget. A recurring, if rarely named, strategy for meeting this budget is the classical divide-and-conquer (D&C) paradigm: partition the probl...

Real-Time Decision Processing in Artificial Intelligence Systems: A Survey of Scheduling, Inference, and Security Foundations

Abstract—Real-time artificial intelligence (AI) decision systems must satisfy three constraints that conventional machine learning pipelines rarely address together: bounded inference latency, deterministic execution under a real-time operating system (RTOS), and resilience against adversarial or unauthorized interfere...

Real-Time Decision Processing in Artificial Intelligence Systems: A Survey of Scheduling, Inference, and Security Foundations

Abstract—Real-time artificial intelligence (AI) decision systems must satisfy three constraints that conventional machine learning pipelines rarely address together: bounded inference latency, deterministic execution under a real-time operating system (RTOS), and resilience against adversarial or unauthorized interfere...

Divide-and-Conquer Decomposition Strategies for Real-Time Artificial Intelligence Decision Systems: A Comparative Survey

Abstract—Real-time artificial intelligence (AI) decision systems must resolve complex inputs — a video frame, a network flow, a multi-agent traffic scene — within a bounded time budget. A recurring, if rarely named, strategy for meeting this budget is the classical divide-and-conquer (D&C) paradigm: partition the probl...

Privacy-Preserving Federated Learning for Industrial Supply Chains via Dynamic Masking and Quality-Aware Secure Aggregation

Initial release accompanying the JISA submission

Towards federated learning for face recognition: a comprehensive review of secure and privacy-preserving techniques and prospective research

A systematic, PRISMA-guided review of 50 publications released between 2020 and 2026, identified through searches of IEEE Xplore, ACM Digital Library, SpringerLink, arXiv, Google Scholar, and DBLP, on face recognition frameworks that use federated learning.

Intelligent Real-Time Healthcare and Biomedical Monitoring Systems: A Narrative Review of AI, IoT, and Emerging Technologies

Background: Chronic disease management, an ageing global population, and the aftermath of the COVID-19 pandemic have pushed real-time, continuous health monitoring from a research curiosity toward routine clinical practice. Artificial intelligence (AI), the Internet of Medical Things (IoMT), edge computing, and next-ge...

Privacy-Preserving Federated Learning for Industrial Supply Chains via Dynamic Masking and Quality-Aware Secure Aggregation

Add Zenodo DOI to README and CITATION.cff

Dual-angle perception partition aggregation federated learning for multi-center multi-modality contrast-free liver tumor segmentation

Uncertainty-aware Bayesian federated learning for low-resource neural machine translation at the edge

Edge translation systems must balance limited computation, data locality, and weak performance on low-resource language pairs. This study presents Multitask Bayesian Federated Learning (MT-BayesFL), a multilingual translation framework that combines a lightweight shared encoder, task-specific decoders, matrix-normal po...

Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture

Artificial-intelligence-enabled fertilizer and fertigation recommendation has progressed from single-task crop or fertilizer classification toward integrated decision-support architectures. However, predictive classification, agronomic dose calculation, irrigation scheduling, explainability, real-time sensing, and farm...