federated learning

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

ABSTRACT 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 approxim...

FedSecure-IoT: Privacy-Preserving Intrusion Detection for IoT Networks Using Hybrid Deep Learning, Federated Learning and Homomorphic Encryption

FedSecure-IoT is a privacy-preserving intrusion detection framework for IoT networks. It combines a hybrid CNN-LSTM-DNN classifier, federated learning with Federated Averaging (FedAvg), and Paillier homomorphic encryption for protecting model updates during aggregation. On traffic derived from the CICIoT2023 dataset (8...

Ingénierie Métrique-Affine : Domaines Transverses et Variantes Applicatives

Résumé (FR)Ce document, produit avec l’assistance de Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive volontaire (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables sur les brevets : art. 54(2) CBE (Convention sur le brevet...

AI applications in drug discovery and personalized medicine

The era of artificial intelligence (AI) in drug discovery and personalized medicine is bringing a new twist to the healthcare field, enhancing the ability to identify the target, develop drugs, and design personal treatment regimens when taking into account the profile of a particular patient. They allow for incorporat...

Artificial Intelligence in the Diagnosis, Treatment, and Management of Rare Diseases: Opportunities and Challenges from a Latin American Perspective

Rare diseases collectively affect between 3.5 and 5.9% of the global population. Yet, patients still endure an average diagnostic interval of five to seven years before receiving an accurate explanation of their condition. Approximately 80% of these disorders have a genetic origin, and nearly 95% lack approved disease-...

Dynamic weighted federated learning with client level differential privacy for DDoS detection in IoT networks

A client-level differential privacy-dynamic weighted federated learning (DWFL) framework that applies Gaussian-mechanism noise with Renyi differential privacy (DP) composition and privacy amplification by subsampling, combined with a quality-score-driven weighted aggregation mechanism, within a single unified architect...

Adaptive privacy-preserving federated learning framework for real-time cybersecurity threat detection in distributed IoT networks

This paper presents an innovative Adaptive Privacy-Preserving Federated Learning framework specifically designed for real-time cybersecurity threat detection in distributed IoT networks, offering a practical framework for real-world deployment in infrastructure-critical applications.

A privacy preserving federated deep learning system for early financial fraud detection and prevention

The proposed DP framework integrates automated mitigation unified risk fusion, guideline-based control verification, and recommendation reporting to offer proactive fraud prevention and comprehensive risk assessment and achieves superior accuracy, precision, recall, and F1-score.