federated learning

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...

Optimization of federated learning protocols for enhanced collaboration in healthcare data analysis

Foundation Models and Vision-Language Models in Medical Imaging: A Review

Foundation models (FMs) and vision-language models (VLMs) have emerged as a transformative paradigm in artificial intelligence, reshaping the landscape of medical image analysis and clinical decision support. Building upon large-scale pretraining on heterogeneous datasets, these models exhibit unprecedented generalizat...

The Impact of Cyberworthiness on Corporate Organisations and Defence Organizations

The growth of interconnected digital systems and the Internet of Things (IoT) has enlarged the cyber-attack surface of modern corporate and defence organisations. In this setting, cyberworthiness denotes a system's capacity to operate securely, reliably, and continuously under adversarial conditions. This thesis treats...

Deep Learning and Artificial Intelligence: Conceptual Foundations of Pattern Recognition and Security Applications

The rapid growth of digital data and the increasing complexity of security threats have expanded the use of artificial intelligence, machine learning, and deep learning in modern security systems. This chapter presents the conceptual foundations of deep learning and pattern recognition and examines their roles in secur...

Health-aware federated contrastive representation learning for generalizable state-of-health estimation of lithium-ion batteries

Federated Learning and Transformer Model‐Based Hybrid AI Model for Predicting Crop Yields

Timely and efficient management of agricultural practices and subsequently predicting crop yields are fundamental elements in the quest to mitigate global food insecurity. Conventional sampling and secondary historical records, which are usually relied upon, tend to be inaccurate and inflexible to current trends. This...

Fed-SecureCvT: optimized federated learning with cryptographic protection and transformer-based intrusion detection for HER

Code and artifacts for: Auditing Explanation Faithfulness in Privacy-Preserving Federated Intrusion Detection

This archive accompanies the manuscript "Auditing Explanation Faithfulness in Privacy-Preserving Federated Intrusion Detection". It contains the source code, the seeded configuration of every run, per-run results, attribution tensors, model checkpoints, summary tables, figure code, supplementary tables and the append-o...

FedSwarm: An Ant-Colony Metaheuristic for Federated Aggregation Weights

Code, experiments, and paper materials for FedSwarm, which studies whether an ant-colony metaheuristic over the federated-learning aggregation weight vector improves on fixed and learned weighting for non-IID, multi-site brain-tumour MRI classification. See paper/00_ABSTRACT.md and HANDOVER.md in this repository for th...