federated learning

Raw training artifacts for "Benign Exclusion and Consensus-Test Limits in Heterogeneous Federated Learning"

Raw archive behind the manuscript's tables and checkpoint analyses: per-run records of the canonical campaign, the client-update matrices saved at rounds 0, 9 and 29, and the frozen source snapshots that produced them. Code, summary tables and the scripts that regenerate them are in the project repository, https://gith...

Artificial Intelligence in Dentistry: Integrated Applications and Specialty-Specific Advances

Artificial intelligence (AI) is transitioning from a supportive tool to a core component of clinical decision-making in dentistry. This review synthesizes advances in deep learning and machine learning for oral disease diagnosis, orthodontic treatment prediction, pediatric oral health management, adolescent psychologic...

CIVILIZATION EVOLUTIONARY LEARNING, UNKNOWN CARTOGRAPHY & SELF-REVISING FUTURES AT THE LIMIT Meta-Learning Across Lineages, Research Phylogenies, Adaptive Commons, Multi-Speed Residents, Deep-Time Memory, Successor Autonomy, and Handoff

CIVILIZATION EVOLUTIONARY LEARNING, UNKNOWN CARTOGRAPHY & SELF-REVISING FUTURES AT THE LIMIT Meta-Learning Across Lineages, Research Phylogenies, Adaptive Commons,Multi-Speed Residents, Deep-Time Memory, Successor Autonomy, and Handoff Feng Cheng-en (33) x Starli When can a civilization-of-civilizations learn from its...

Béton Roman Augmenté et Architecture Décentralisée

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. 54(2) CBE (Convention sur le brevet européen), art. L...

Hyper-Personalization vs. Privacy Paradox: Balancing AI-Driven Customer Insights with Consumer Data Ethics and Trust

Abstract: The rapid proliferation of artificial intelligence and machine learning has enabled organizations to achieve unprecedented levels of hyper-personalization, transforming how brands interact with consumers. However, this capability intensifies the privacy paradox, a phenomenon where consumers express profound c...

Hyper-Personalization vs. Privacy Paradox: Balancing AI-Driven Customer Insights with Consumer Data Ethics and Trust

Abstract: The rapid proliferation of artificial intelligence and machine learning has enabled organizations to achieve unprecedented levels of hyper-personalization, transforming how brands interact with consumers. However, this capability intensifies the privacy paradox, a phenomenon where consumers express profound c...

Federated Machine Learning for Privacy-Preserving Intelligent Systems

Federated Machine Learning for Privacy-Preserving Intelligent Systems

FedTrust-GNN: Federated Learning with Blockchain Trust and Graph Neural Networks for Decentralized Modeling

Centralized user modeling systems inherently violate privacy regulations, with catastrophic failure points, opaque trust management mechanisms, and vulnerability to complex adversarial strategies such as poisoning attacks, model inversions, and membership inference. Federated learning (FL) approaches address data priva...

FedTrust-GNN: Federated Learning with Blockchain Trust and Graph Neural Networks for Decentralized Modeling

Centralized user modeling systems inherently violate privacy regulations, with catastrophic failure points, opaque trust management mechanisms, and vulnerability to complex adversarial strategies such as poisoning attacks, model inversions, and membership inference. Federated learning (FL) approaches address data priva...

Rapport Multidisciplinaire sur le Revenu Universel

Abstract ENThis document, produced with the assistance of ChatGPT o3 and ChatGPT 5 Thinking, is released under the Apache 2.0 licence. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: art. L 611-11 CPI (French Intellectual Property...

Distributed Machine Learning and Federated Edge Computing for IoT

The growing scale and heterogeneity of Internet of Things (IoT) environments are shifting machine learning (ML) from centralized cloud infrastructures toward distributed intelligence across the IoT–edge–cloud continuum [...]