federated learning

From Two Players to a Galactic Network: Entry, Extinction, Coalitions, Cliques and Spatial Topology in Interstellar Strategic Interaction

Dark Forest arguments commonly reduce interstellar strategy to a bilateral encounter between two persistent civilisations. A galaxy, however, is an open population in which civilisations enter, disappear, form selective relationships and occupy a spatially constrained network. This paper develops a continuous-time Gala...

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)

Reliability Beyond Accuracy in Crop Classification Benchmarks (Supplementary Materials)Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as agronomic recomme...

Are Our Current Rational Decision-Making Models Truly Rational? A Critical Analysis and a New Neurobiological Framework

This paper critically examines the foundational assumptions of rational decision-making models and finds them to be systematically and comprehensively flawed. Through a rigorous analysis of empirical evidence from behavioral economics and neuroscience, we demonstrate that traditional models, such as Expected Utility Th...

Are Our Current Rational Decision-Making Models Truly Rational? A Critical Analysis and a New Neurobiological Framework

This paper critically examines the foundational assumptions of rational decision-making models and finds them to be systematically and comprehensively flawed. Through a rigorous analysis of empirical evidence from behavioral economics and neuroscience, we demonstrate that traditional models, such as Expected Utility Th...

Can Democracy Survive in a Hyperconnected World?

This paper explores the complex and often contradictory relationship between hyperconnectivity and democracy. Drawing on novel theoretical frameworks inspired by quantum mechanics, network science, and temporal dynamics, we argue that the hyperconnected world is not simply an extension of the classical political landsc...

Can Democracy Survive in a Hyperconnected World?

This paper explores the complex and often contradictory relationship between hyperconnectivity and democracy. Drawing on novel theoretical frameworks inspired by quantum mechanics, network science, and temporal dynamics, we argue that the hyperconnected world is not simply an extension of the classical political landsc...

Is the Algorithm an Epistemic Agent? A Critical Analysis of Computational Epistemology and the Emergence of Algorithmic Agency

The question of whether algorithms can be considered epistemic agents represents one of the most profound challenges at the intersection of philosophy of mind, epistemology, and artificial intelligence. This paper develops a novel theoretical framework for understanding algorithmic epistemic agency through the introduc...

Is the Algorithm an Epistemic Agent? A Critical Analysis of Computational Epistemology and the Emergence of Algorithmic Agency

The question of whether algorithms can be considered epistemic agents represents one of the most profound challenges at the intersection of philosophy of mind, epistemology, and artificial intelligence. This paper develops a novel theoretical framework for understanding algorithmic epistemic agency through the introduc...

Privacy-Preserving AI: Federated Learning, Differential Privacy, and Data Minimization: Production Architecture, Current Research, Real-World Use Case, Implementation, and Verification

Privacy-Preserving AI: Federated Learning, Differential Privacy, and Data Minimization: Production Architecture, Current Research, Real-World Use Case, Implementation, and Verification Author: Sonu Kumar Singh (Senior Consultant — Cloud & AI Solutions Architecture, Capgemini US LLC) Professional Affiliation: Capgemini...

marambhuvana-cpu/RobustTrust-FL: RobustTrust-FL v1.0.0

About RobustTrust-FL RobustTrust-FL is a trust-aware and attack-resilient federated learning framework for scalable privacy-preserving distributed intelligence. Key components PrivacyGuard: Privacy-preserving client updates using clipping and differential privacy. AttackShield: Detection of suspicious and potentially m...

Scaling limits of federated learning for vision-based road damage detection across countries