federated learning

Deep Learning for Proxy Zero-Day Intrusion Detection: A Systematic Review, Taxonomy, and Evaluation Framework

Machine Learning-Driven Optimization of Multiphase Chemical Processes for Energy-Efficient and Sustainable Manufacturing

There are many industrial chemical manufacturing processes that involve multi-phase chemistry, such as gas–liquid, liquid–liquid, gas–solid, solid–liquid and three-phase systems, and these are among the most energy-intensive and complex unit processes to optimise, due to the complex, nonlinear and often unmeasurable in...

Artificial Intelligence for Smart Grid and Renewable Energy Management: A Review of Techniques, Applications, and Future Directions

Intermittent renewable generation, volatile loads, and concerns over grid stability have become defining features of modern power systems. Addressing these challenges with Artificial Intelligence (AI) and Machine Learning (ML) is attracting growing attention, including through better forecasting, automated demand respo...

AI-Driven Digital Twin Frameworks for Predictive Maintenance and Closed-Loop Remote Control in Smart Factories: A Structured Review

The increasing connectivity of industrial machines has changed maintenance from a largely schedule-driven activity into a continuous, data-driven process. Digital Twin (DT) technology supports this transition by providing a dynamic virtual representation of a physical asset that is continually updated using operational...

Cloud‐Native and Distributed Systems for Efficient and Scalable Large Language Models—A Research Agenda

ABSTRACT Objective Large Language Models (LLMs) are rapidly transforming artificial intelligence applications, but their unprecedented computational, memory, communication, and data‐management demands pose significant challenges to existing computing infrastructures. Traditional cloud and distributed systems were not d...

Model Optimization and Quantization for Efficient Neural Networks

Deep learning has transformed fields such as computer vision, natural language processing, and autonomous systems. However, the complexity of these models poses challenges for deployment on edge devices and real-time applications due to high computational and memory demands. Model optimization techniques, such as pruni...

Foundations of Federated Learning

Security and Privacy‐Preserving Tiny Machine Learning

This work examines the privacy and security issues with tiny machine learning (TinyML) and proposes potential solutions to improve dependability and trust in practical applications. A thorough analysis of current security risks is conducted, including data leakage, model extraction, and adversarial assaults. Furthermor...

A Federated Agricultural Market Intelligence Framework for Distributed Price Discovery and Inflation Monitoring

Agricultural markets often experience considerable differences between the prices received by farmers and the prices paid by consumers. These disparities arise from fragmented supply chains, limited market transparency, transportation costs and the involvement of multiple intermediaries. As a result, farmers may receiv...

CAFA: Coverage-Adaptive Contribution-Aware Federated Aggregation for ECG Classification (code and reproducibility bundle)

Source code, experiment configurations, and a slim per-seed results bundle that reproduce every table and figure in the paper 'Coverage-Adaptive Contribution-Aware Federated Aggregation for ECG Classification under Heterogeneous Label Quality' (Biomedical Signal Processing and Control). CAFA is an aggregation-only fede...

Federated Fine-Tuning of Foundation Models (Fed-LLMs): Parameter-Efficient Adapters, Split Learning, and Gradient Privacy Bounds

Abstract—Fine-tuning Large Language Models (LLMs) and Vision Foundation Models on private, decentralized domain data is essential for specialized healthcare, legal, and financial enterprise applications. However, centralizing domain-specific text corpora triggers catastrophic data privacy violations under stringent reg...

THE EL-RAKHAWI COMPREHENSIVE BIOLOGICAL ARTIFICIAL INTELLIGENCE FRAMEWORK: UNIFIED MULTI-OMIC FOUNDATION MODELS FOR CAUSAL DISEASE MODELING AND PERSONALIZED THERAPEUTIC DISCOVERY

The El-Rakhawi Comprehensive Biological AI (ECB-AI) Framework revolutionizes medical research by transcending fragmented, correlational models. It introduces a Unified Multi-Omic Foundation Model mapping genomics, proteomics, and clinical data into a topologically protected Riemannian manifold. Its core innovation, the...