federated learning

Blockchain–federated learning integration in the internet of vehicles: analysis, taxonomies, and future directions

Communication-efficient federated analytic class-incremental learning via orthogonal residual compensation

Study-level characteristics and quality scores of the 74 Tier-1 studies in a systematic review of federated learning for ECG-based cardiac monitoring

This deposit contains the study-level data supporting the systematic literature review “Federated Learning for ECG-Based Cardiac Monitoring: A Systematic Literature Review” (Bouayad, Ayache, Nasser), conducted following PRISMA 2020. The review covers publications from January 2020 to 7 March 2026, retrieved from IEEE X...

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...

EduBot: Integrating Federated Learning and RAG for Educational Document Analysis

There is a growing demand for intelligent and secure document management systems in the education sector, driven by the exponential growth of digital content. Nevertheless, classic centralized machine learning methods pose major privacy challenges. In this study, we present EduBot, a decentralized intelligent framework...

Participation Bias Correction for Byzantine-Robust Federated Learning Under Heterogeneous Client Availability

Federated learning enables distributed clients to collaboratively train a global model without sharing their private data. However, Byzantine robustness becomes more challenging under heterogeneous client availability, since Byzantine clients with higher availability can participate more frequently than honest clients...

Platform-free proof of federated learning consensus blockchain with optimized neural network-based electronic health records storage and sharing scheme

Securing Intelligent Edge Ecosystems

With the advent of Internet of Things technologies and intelligent edge computing systems, the surface of cyber-attacks has been increasingly growing, which results in the problem of network security in distributed systems. This chapter will provide a review of intelligent intrusion detection methods using AI for secur...

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...