federated learning

Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems

Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental int...

AI-Driven Personalization and Adaptive Learning in Decentralized Environments

With the rapid growth of digital technologies, the traditional education system is transforming into intelligent and decentralized learning environments that support personalized and adaptive learning experiences. Conventional educational platforms often face challenges such as scalability, learner engagement, data own...

Fair Bayesian Stackelberg Federated Learning Under Strategic and Heterogeneous Client Participation

Federated learning must accommodate statistical heterogeneity, costly client participation, and distinct fairness objectives for model performance and participation frequency. We introduce Fair Bayesian Stackelberg Federated Learning (FBS-FL), in which a server maintains beliefs over private client cost types and selec...

FedStaP: Shared Feature Statistics and Prior-Calibrated Training for Non-IID Federated Intrusion Detection in IoT Networks

Federated learning allows IoT gateways to train a shared intrusion detector without exporting traffic records, but gateways observe different attack classes and scale their flow features differently. Stateful optimisers such as SCAFFOLD correct the resulting client drift at the cost of per-client memory and twice the p...

P146: Federated analytics across heterogeneous stores: Cross-cloud comparative study

Federated analytics across heterogeneous stores: Cross-cloud comparative study Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Credential: Member, IEEE (Membership # 102728576) | ORCID: 0009-0002-9180-4946 Abstract Cloud data platforms are no longer used o...

Web4 and Autonomous AI Agents in Decentralised Education Ecosystems Transforming Intelligent and Personalized Learning

The rapid emergence of Web4 technologies, autonomous AI agents, blockchain systems, and immersive virtual environments is redefining the future of education. Decentralized education ecosystems are enabling intelligent, learner-centric, secure, and adaptive educational experiences beyond traditional digital learning mod...

BlockGuard: Serverless Selective Homomorphic Encryption for Federated Learning

Introduction to Computational Analysis in Medical Imaging

The exponential growth of digital imaging modalities like CT, MRI, Ultrasound, PET and digital pathology has produced huge amount of high dimensional data. This data having powerful insights need advanced computational techniques for accurate interpretation and clinical decision making. In the clinical setting, sophist...

Blockchain Federated Learning Architecture

Customizing education technology with data-driven AI undermines student privacy. Centralized analytics risk illegal data access, single points of failure, and loss of institutional sovereignty. BFL-EdArch, a four-layer blockchain-enabled federated learning architecture, enables privacy-preserving collaborative intellig...

Blockchain-Driven Federated Learning for Privacy-Preserving Smart Classrooms

The adoption of data driven technologies in education has raised concerns about privacy, transparency and trust in centralized education systems. Artificial intelligence and learning analytics are used to personalized learning, however, their reliance on centralized data systems raises questions about privacy and secur...

Intelligent defense at the edge: a comprehensive survey of federated learning, TinyML and explainable AI for intrusion detection in IoT and IIoT ecosystems

The proliferation of Internet of Things (IoT) and Industrial Internet of Things (IIoT) technologies has fundamentally transformed contemporary computing infrastructures by interconnecting large heterogeneous devices, sensors, embedded systems, and cyber-physical platforms. These ecosystems support diverse applications...

Federated and Differentially Private Learning for Root Cause Classification Across Heterogeneous Semiconductor Fabs

Federated learning (FL) can train a shared model without pooling client records, but it does not by itself provide differential privacy (DP). We compare centralized training, local-only training, federated averaging (FedAvg), and client-level DP-FedAvg on a fully released synthetic benchmark of free-text root-cause-ana...