federated learning

AI-Powered Crowdsourcing for Effective Disaster Relief Coordination: A Systematic Review

Post-disaster coordination is often hindered by information fragmentation. This paper reviews AI and crowdsourcing integration to address these challenges. We examine three core areas: situational awareness via text and geospatial data, logistics coordination using predictive modelling, and ethical deployment concerns....

Applications and challenges of artificial intelligence in cancer management

Artificial intelligence (AI) is increasingly transforming cancer management by enabling the analysis of complex multimodal data generated across the cancer care continuum. This structured narrative review synthesizes current applications of AI, machine learning, and deep learning in cancer detection, imaging diagnosis,...

Transcending Memory Constraints in Federated Learning via Sequential Block-Wise Training

Federated Learning (FL) enables collaborative model training across distributed clients while preserving data privacy. However, the substantial memory footprint required for model training imposes a critical memory wall on resource-constrained edge devices, severely limiting their participation and compromising the inc...

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...

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...