federated learning

Artificial Intelligence for Net-Zero and Climate-Resilient Sustainable Cities: Technologies, Challenges, and Future Perspectives

Urbanization, climate change, increasing energy demand, and rapid infrastructure development have created significant challenges for achieving sustainable and resilient cities. Artificial intelligence (AI) has emerged as a transformative technology that enables data-driven decision-making, intelligent automation, and e...

Future Directions in Tiny Machine Learning

Tiny machine learning (TinyML) represents a transformative frontier in the evolution of artificial intelligence (AI), merging machine learning capabilities with ultra-low-power embedded systems to enable real-time inference at the edge. As data generation accelerates across billions of Internet of Things (IoT) devices,...

Hands‐On Project and Case Studies

TinyML is a technology that incorporates artificial intelligence into edge machines, which allows machine learning models to be executed on very low-power microcontrollers with small memory and processing units. In contrast to cloud-based AI-based solutions, TinyML does not require the cloud to run inference on an embe...

Federated learning with adaptive reliability aware aggregation for privacy preserving healthcare IoT systems

Healthcare Internet of Things (IoT) devices generate vast amounts of sensitive patient data, creating significant privacy risks and regulatory challenges when processed through centralized architectures. Federated Learning (FL) offers a promising solution by enabling decentralized model training without sharing raw dat...

Equity-Aware Agentic AI Architectures and Governance With SDG-Aligned Healthcare Innovation

Agentic AI marks a shift from static forecasting to autonomous, goal-oriented systems for healthcare workflows. Unlike conventional AI models, agentic AI integrates adaptive learning with planning, execution, monitoring, and coordination within multi-agent architectures. This chapter explores how these systems collabor...

Multimodal based Brain Tumor Classification using Clustered Federated Learning in Edge based IoT Network

A brain tumor is characterized by the uncontrolled growth of cells that causes a severe impact on the quality of life. Nowadays, various methods based on multimodal medical images are used to identify brain tumors. Meanwhile, these methods require extensive amounts of labeled information to identify disease tissues and...

Supporting MSME Innovation: An Adviser’s Manual Developed for the Philippine Department of Science and Technology

This Productivity Insights publication outlines four practical AI strategies specifically crafted for small and medium-sized manufacturing enterprises: (1) predictive maintenance and AI-based visual inspection for quality control, (2) integration through digital twins and cyber-physical production systems, (3) human-in...

A systematic review of non-contact medical IoT systems for physiological biomarker monitoring

Non-contact medical Internet of Things systems measure physiological biomarkers such as heart rate (HR), respiration rate (RR), temperature, oxygen saturation (SpO₂), and blood pressure (BP) without physical contact with the patient. These systems combine sensing modalities including cameras, radar, Wi-Fi, acoustic...

Data-aware disk-covering clustering for federated learning in WSNs: communication overhead and energy analysis

Spatiotemporal Machine Learning for Smart Cities: A Comprehensive Review of Models and Applications

The rapid growth of sensor networks, satellite platforms, Internet-of-Things (IoT) devices, and urban monitoring systems has produced vast volumes of spatiotemporal data. Modeling such data is central to applications ranging from air-quality forecasting and climate monitoring to traffic prediction and urban planning, y...

Machine Learning Techniques for Malware Detection

Malware is one of the major threats to modern computer systems, mobile devices, cloud platforms, and networks. Traditional signature-based antivirus systems are effective against known malware but often have difficulty detecting new, polymorphic, obfuscated, and zero-day variants. Machine Learning (ML) provides a data-...

FedDCDS: a dual-phase secure federated learning framework with dynamic clustering and adaptive delivery

FedDCDS introduces a novel dual-phase secure federated learning framework designed to address the intertwined challenges of statistical heterogeneity, system heterogeneity, and security threats in large-scale edge computing environments. By integrating a dynamic spectral clustering mechanism, the framework dynamically...