federated learning

Mapping Successful AI Application in Well Delivery to Domain Knowledge, SME Capacity in Operators and Their Suppliers

Abstract Major operators and service companies sustain deep expertise by funding robust hiring and training to develop in-house Subject Matter Experts (SMEs). Conversely, smaller players lack the capital for permanent specialist benches. Their fluctuating needs and limited funding force them to purchase knowledge from...

A privacy-preserving federated deep learning framework for real-time fraud detection in decentralized financial systems

The rapid growth of decentralized financial systems has introduced sophisticated fraud that conventional detection methods struggle to identify, due to fragmented data, strict privacy regulations, and the need for real-time response. This study proposes Federated Differentially Private Fraud Detection (FedDP-FD), a pri...

Artificial Intelligence and Data Science Applications in Anaerobic Digestion: Monitoring and Optimization Strategies

Abstract Anaerobic digestion (AD) is a significant biological process that transforms organic waste into renewable biogas and nutrient-dense digestate. This can be returned as fertilizer, which promotes waste management and renewable energy production. However, typical monitoring relies on offline laboratory measuremen...

FedCuraLM: Reliability-Calibrated and Resource-Aware Label Auditing with Small Language Models for Non-IID Federated AIoT

Federated Artificial Intelligence of Things (AIoT) activity-recognition systems learn from decentralized wearable and smart-home records with potentially incorrect labels, non-IID client distributions, and unequal edge-device resources.Our objective is to improve downstream learning from these labels while limiting har...

BANAR: Self-feedback stealthy backdoor attack in federated learning

Semantic communication for the internet of underwater things: Architectures, applications, challenges, and future directions

The Internet of Underwater Things (IoUT) supports marine sensing, environmental monitoring, subsea inspection, and autonomous underwater operations. However, IoUT communication is constrained by limited bandwidth, long propagation delay, time-varying underwater channels, intermittent connectivity, and strict energy bud...

AI‐Driven Multimodal Fusion for Predictive Disease Modeling and Clinical Insight Generation

Data-driven approaches in disease modeling have been one of the important criteria where artificial intelligence (AI), machine learning (ML), and big data analytics are some of the latest technologies for predicting and managing the different types of diseases. For these methodologies, diverse datasets have been integr...

Blockchain-Driven FL and Big Data Mining Concepts with Spatial Attention-Based Adaptive Deep Learning Model for Smart Tourism

In smart tourism planning, tourism-related devices are used to maintain tourist data and manage hotel room bookings. Smart tour management using deep learning models has overfitting issues due to the complexity of large-scale data. The major issues in traditional tourism sectors are data privacy, interoperability, and...

FedCardioGuard: Quality- and Uncertainty-Aware Personalized Federated Learning with Rare-Class Generalization Audits for ECG Classification

EL-RAKHAWI DOCTRINE OF QUANTUM CELLULAR DIGITAL TWINS A UNIFIED MATHEMATICAL FRAMEWORK FOR WHOLE-CELL HUMAN SIMULATION AND IN-SILICO CLINICAL TRIALS

EL-RAKHAWI DOCTRINE OF QUANTUM CELLULAR DIGITAL TWINS: SUMMARY This revolutionary framework presents a unified mathematical approach for whole-cell human simulation and in-silico clinical trials, addressing fundamental limitations in drug discovery and biological research. Four foundational pillars: (1) Cellular Cohere...

IoT-Enabled Instrumentation Architectures for Predictive Maintenance and Remote Monitoring in Large-Scale Automated Industrial Plants

The fast industrial digitalization process results in the implementation of Internet of Things (IoT) technologies into the big automated plants to improve its operational precision, reliability, and sustainability. The paper presents a novel IoT-based instrumentation architecture to predictive maintenance and remote mo...

Self-Evolving Adaptive Resource Management Framework for Resilient 6G-IoT Architectures Using Federated Edge Intelligence and Semantic Communication

The massive rise of hyperconnected devices, ultra-reliable low-latency communications (URLLC), and autonomous systems on the 6G-IoT front line, have called for a drastic change to resource management methodologies. In this paper we present a pioneering work the S-EARM (Self-Evolving Adaptive Resource Management) framew...