federated learning

Artificial Intelligence in Drug Development

The pharmaceutical industry faces persistent challenges in discovering and developing safe, effective, and affordable medicines. Artificial intelligence (AI) has emerged as a transformative computational technology capable of supporting multiple stages of the drug-development pipeline. Machine learning, deep learning,...

Beyond Robustness: Uncovering Blind Spots in Federated Learning Defenses

THE EL-RAKHAWI THEORY OF SYMBIOTIC URBAN INTELLIGENCE AND REGENERATIVE INDUSTRIAL ECOSYSTEMS From Self-Healing Cities to the Sovereign Cognitive State A Unified Framework Integrating Regenerative Urban Metabolism, Digital Immune Systems, and Living Industrial Twins for the Next Generation of Sustainable Smart Nations

The El-Rakhawi Theory of Symbiotic Urban Intelligence and Regenerative Industrial Ecosystems (DOI: 10.5281/zenodo.23192881) integrates urban metabolism, sovereign digital governance, and precision manufacturing into a unified self-healing national ecosystem. Three pillars: (1) Regenerative Urban Metabolism with genetic...

A sustainable platform for federated health data access, AI innovation, and regulatory acceptance in alignment with the European Health Data Space principles

The IDERHA ( I ntegration of Heterogeneous D ata and E vidence towards R egulatory and H TA A cceptance) project aims to enhance medical research by establishing one of Europe’s first pan-European, disease-agnostic health data spaces. Aligned with the European Health Data Space (EHDS) principles, IDERHA addresses criti...

Deep Learning for Cybersecurity

The rapid progress in digitization of core industries has increased the size of the threat horizon to the point where cybersecurity measures must be more articulate, adaptive, and intelligent. As cyber-attacks are increasingly evolving and become more complex, attempts to secure networks with traditional methods are be...

Explainable federated learning for spatio-temporal stress inference in distributed agricultural monitoring

Agricultural crops experience environmental and physiological stressors that evolve across space and time. These processes are only indirectly observable through imaging and are frequently monitored across geographically distributed sites with heterogeneous conditions and restricted data sharing. This study formulates...

A Context-Aware Hybrid Local Differential Privacy–Federated Learning Framework for Digital Footprint Protection

Digital services increasingly depend on information generated from users’ online activities, yet the same traces that make these services useful can expose individuals to re-identification and inference. This study develops and evaluates a context-aware Hybrid Local Differential Privacy–Federated Learning (LDP-FL) appr...

Artificial Intelligence in genetic healthcare: toward adaptive and systems-level genomic medicine

Artificial intelligence is reshaping genetic healthcare through increasingly sophisticated approaches to variant interpretation, disease prediction, biomarker discovery, gene editing, and precision therapeutics. Yet these applications remain fragmented, often treating genomic and clinical information as static observat...

Explainable federated learning for spatio-temporal stress inference in distributed agricultural monitoring

Agricultural crops experience environmental and physiological stressors that evolve across space and time. These processes are only indirectly observable through imaging and are frequently monitored across geographically distributed sites with heterogeneous conditions and restricted data sharing. This study formulates...

Finite payload bias and delivery correction in wireless federated learning: result archives and checkpoints

Result archives, checkpoints, tables and figures from the three studies in the paper "Finite payload bias and delivery correction in wireless federated learning" (UCI HAR with subject clients, UCI HAR with Dirichlet clients, PAMAP2 with subject clients; 12 seeds each). Produced by the code archived at DOI 10.5281/zenod...

Federated Learning for Medical Image Analysis Using Convolutional Neural Networks

Medical image segmentation is an important part of healthcare since it lets doctors clearly see anatomical features and diseased areas for diagnosis, therapy planning, and monitoring. CNN works fine with different types of medical images like MRI, CT and ultrasound for segmentizing it precisely. In this chapter all ava...

Fuzzy-logic enhanced internet of things model for uncertainty-adaptive medical image segmentation

Deep learning (DL) models for brain tumour segmentation (BTS) typically produce a fixed decision boundary and do not allow the uncertainty they estimate to impact the segmentation decision (SD). This work presents a FUZIONet-Med: A Fuzzy Uncertainty-Adaptive IoT Edge Intelligence Model for Brain Tumor Segmentation in w...