federated learning

Federated Soft Clustering via Generalized Total Variation Minimization — code and data

We study federated soft clustering over federated learning (FL) networks of devices that each hold a private local dataset and fit a personalized Gaussian mixture model (GMM). Generalized total variation minimization (GTVMin) couples the local maximum likelihood problems through a graph regularizer that penalizes a dis...

Federated Soft Clustering via Generalized Total Variation Minimization — code and data

We study federated soft clustering over federated learning (FL) networks of devices that each hold a private local dataset and fit a personalized Gaussian mixture model (GMM). Generalized total variation minimization (GTVMin) couples the local maximum likelihood problems through a graph regularizer that penalizes a dis...

Parallel and Distributed Training of Variational Quantum Algorithms on NISQ Systems: A Systematic Review of Scaling, Noise, and Cost

Background: Parallel and distributed execution is proposed to relieve throughput, trainability and resource constraints in variational quantum algorithms (VQAs), but the label “parallel” covers physically and statistically different interventions. We assessed what has actually been measured on noisy intermediate-scale...

Leveraging Cross-Silo Federated Learning in Process Mining

Leveraging Cross-Silo Federated Learning in Process Mining

Fed fusion-IDS: a privacy-preserving federated intrusion detection system with multi-objective feature optimization and explainable deep learning

FedFusion-IDS is a new privacy-preserving federated framework for intrusion detection based on federated learning, multi-objective feature selection with NSGA-II and SHAP second-level filter, hybrid deep learning architecture with CNN, BiLSTM, and Attention mechanisms, explainability through SHAP and LIME, and zero-day...

Parallel and Distributed Training of Variational Quantum Algorithms on NISQ Systems: A Systematic Review of Scaling, Noise, and Cost

Background: Parallel and distributed execution is proposed to relieve throughput, trainability and resource constraints in variational quantum algorithms (VQAs), but the label “parallel” covers physically and statistically different interventions. We assessed what has actually been measured on noisy intermediate-scale...

Parallel and Distributed Training of Variational Quantum Algorithms on NISQ Systems: A Systematic Review of Scaling, Noise, and Cost

Background: Parallel and distributed execution is proposed to relieve throughput, trainability and resource constraints in variational quantum algorithms (VQAs), but the label “parallel” covers physically and statistically different interventions. We assessed what has actually been measured on noisy intermediate-scale...

Similarity-Promoting Resilient Adaptive Aggregation in Peer-to-Peer Machine Learning

Privacy-Preserving Fraud Intelligence for Kenya's Account-to-Account Payment Ecosystem

This whitepaper proposes a privacy-preserving federated graph intelligence architecture for fraud detection across Kenya’s account-to-account payment ecosystem, using PesaLink as the principal infrastructure context. The research addresses a structural limitation of institution-level fraud detection: fraudulent activit...

Privacy-Preserving Fraud Intelligence for Kenya's Account-to-Account Payment Ecosystem

This whitepaper proposes a privacy-preserving federated graph intelligence architecture for fraud detection across Kenya’s account-to-account payment ecosystem, using PesaLink as the principal infrastructure context. The research addresses a structural limitation of institution-level fraud detection: fraudulent activit...

PP-FEO: Privacy-Preserving Federated Ensemble Optimization for Intrusion Detection

This repository contains the source code and reproducibility materials associated with the Privacy-Preserving Federated Ensemble Optimization (PP-FEO) framework for intrusion detection in distributed Cyber-Physical System (CPS) environments. The implementation includes data preprocessing and feature harmonization, the...