federated learning

Cadre Stratégique pour l'Infrastructures Immobilière Éco-Résilience : Déclaration d'Antériorité Complète et Divulgation Habilitante

Résumé FRCe 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 : art. L 611-11 CPI (Code de la propriété intellectuelle fran...

Саморегульований центр операцій безпеки на основі федеративного навчання з постквантовими механізмами захищеної агрегації

Objective. The objective of the research is to develop an architecture for a self-regulating Security Operations Center (SOC) that ensures autonomous adaptation of agents to new threats by combining federated learning with post-quantum secure model aggregation mechanisms based on lattice cryptography, while simultaneou...

AI-Driven Predictive Modeling for Personalized Vitiligo Treatment and Patient Stratification

Vitiligo is hard to personalize because disease activity, anatomical response, immune persistence, and repigmentation capacity vary widely across patients. This chapter proposes a precision-medicine framework integrating longitudinal clinical data, standardized imaging, patient-reported outcomes, biomarkers, transcript...

Self-evolutionary control architecture based on distributed brain-inspired decision making and privacy-preserving PID

Traditional PID control suffers from limited parameter adaptability in complex dynamic systems and poor robustness against nonlinearities and uncertain disturbances. Meanwhile, in distributed power control scenarios, the risk of leakage regarding multi-node sensor data and AI model parameters hinders the practical impl...

Integrating Blockchain, Federated Learning, and Metaverse Ecosystems Into Decentralized Classrooms

FedStaP: Shared Feature Statistics and Prior-Calibrated Training for Non-IID Federated Intrusion Detection in IoT Networks

Federated learning allows IoT gateways to train a shared intrusion detector without exporting traffic records, but gateways observe different attack classes and scale their flow features differently. Stateful optimisers such as SCAFFOLD correct the resulting client drift at the cost of per-client memory and twice the p...

Federated Label Verification: Reproducibility Code and Experimental Results

This software and reproducibility package supports the study “Label-Induced Bias and Verification-Cost Trade-offs in Federated Learning: A Budget-Constrained Crowd-Intelligence Approach.” It contains source code, configuration information, analysis scripts, and final five-seed experimental outputs for federated learnin...

Machine Learning Medical Image Classification and Detection Data Analysis

Medical imaging is a core component of healthcare in the contemporary era, but the sheer scale of digital image data requires effective automated processing. Machine learning methods have become effective tools for classification and detection, both in classic procedures using traditional machine learning models and in...

Foundation of Digital Medical Imaging Technologies

Digital medical imaging has developed as an indispensable modality in the context of modern health care systems, allowing for non-invasive visualization of internal anatomic and physiologic structures to successfully facilitate right diagnosis, treatment planning, and monitoring. In the context of the rapid development...

Empowering Farmers Through Privacy-Preserving AI: A Federated Learning Approach for Soybean Disease Classification

Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning

We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central server to collaboratively learn the optimal stateaction value function. Our goal is to understand whether the sample-efficiency benefits of collaboration can be retained wh...

Operating Envelopes of Synchronous and Asynchronous Federated Learning Under Communication Impairments in Smart-Grid Sensor Networks

Federated learning (FL) for distributed smart-grid monitoring is shaped jointly by statistical heterogeneity and communication timing. This study evaluates protocol operating envelopes rather than proposing a new aggregator. A frozen confirmatory campaign compares deadline-constrained FedAvg, FedAsync, FedBuff, and Fed...