federated learning

AI-Driven Next-Generation DDoS Mitigation in IoT: A Comprehensive Survey of Deep Learning and Large Language Models

The global Internet of Things (IoT) network has experienced unprecedented expansion, reaching 75.44 billion connected devices worldwide. Due to this rapid growth, the IoT ecosystem has become larger but also more vulnerable to various threats in cyberspace. As a result, this situation has accelerated the development of...

A review of recent advances in privacy-preserving federated learning for healthcare applications

The rapid adoption of artificial intelligence (AI) in healthcare, driven by the increasing availability of electronic health records (EHRs), medical imaging systems, genomic data, and wearable devices, has created significant challenges related to patient privacy, data security, and regulatory compliance. Traditional c...

A systematic review of federated learning based intrusion detection systems in software defined networking

FedPoP: federated learning meets proof of participation

Federated learning (FL) offers privacy preserving, distributed machine learning, allowing clients to contribute to a global model without revealing their local data. As models increasingly serve as monetizable digital assets, the ability to prove participation in their training becomes essential for establishing owners...

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...