federated learning

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...

Deep Learning Methods for Cardiovascular Disease Detection in CT and MRI Scans

Cardiovascular diseases remain a leading health concern worldwide and increase the demand for rapid and accurate diagnostic techniques. Although CT and MRI imaging provide excellent cardiac studies, most analysis is slow and inconsistent when performed manually. Deep learning techniques showed strong potential in provi...

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...

Blockchain-Enabled Federated Learning for Healthcare: An Empirical Evaluation of the Integrity Boundary, Byzantine Robustness, and Ledger Overhead

Federated learning enables healthcare institutions to train shared models without pooling patient records, but it does not by itself authenticate participants, verify that model updates arrive unaltered, or provide an auditable record of contributions. Permissioned blockchains are often proposed to address these gaps,...

Speech and Voice Biomarkers for Parkinson’s disease Detection: A Systematic Review of Machine Learning and Deep Learning Models

Federated Swarm Intelligence for Privacy-Preserving Mobile Collaborative Learning

The increase in mobile learning escalates the friction between data-led personalization and safeguarding sensitive, institutionally fragmented student data. Centralized analytics necessitates the aggregation of raw records, which conflicts with privacy regulation and mobile resource limits. A novel federated swarm-inte...

Artificial intelligence in wastewater treatment: critical review of predictive performance, explainability and deployment readiness

Artificial intelligence (AI) has been increasingly adopted in wastewater treatment to support soft sensing, effluent prediction, nutrient removal assessment, membrane monitoring, anomaly detection, greenhouse-gas emission modeling, and anaerobic digestion optimization. This critical review synthesizes approximately 5...