federated learning

FEDERATED LEARNING FOR STRESS DETECTION USING DISTRIBUTED WEARABLE DATA IN PRIVACY PRESERVING HEALTHCARE SYSTEMS

Nowadays, comprehensive, continuous, and reliable monitoring is required in healthcare systems to account for stress-related impacts not only on physical but also on mental health, such as cardiovascular diseases, anxiety, and impaired cognitive function. This research aims to propose a federated learning approach for...

FEDERATED LEARNING FOR STRESS DETECTION USING DISTRIBUTED WEARABLE DATA IN PRIVACY PRESERVING HEALTHCARE SYSTEMS

Nowadays, comprehensive, continuous, and reliable monitoring is required in healthcare systems to account for stress-related impacts not only on physical but also on mental health, such as cardiovascular diseases, anxiety, and impaired cognitive function. This research aims to propose a federated learning approach for...

Internet of Things and Machine Learning for Sustainable Development: A Systematic Review and Conceptual Framework

The integration of the Internet of Things (IoT) and Machine Learning (ML) has emerged as a transformative technological paradigm for addressing global sustainability challenges through intelligent sensing, real-time data analytics, and autonomous decision-making. Although numerous studies have explored IoT-ML applicati...

A federated framework for simulating and predicting malware propagation in network topologies with privacy awareness

In the digital era, security threats from self-propagating malware present a critical challenge for network infrastructure. Although classical epidemiological models are valuable for analyzing malware, their deterministic nature, along with administrative barriers to consolidating raw network data, hinder accurate dist...

Beyond Centralized Diagnosis: A Privacy-Preserving Federated Learning Framework for Robust Pneumonia Detection Under Data Heterogeneity and Class Imbalance

Optimal estimation in private distributed functional data analysis

We systematically investigate the preservation of differential privacy in functional data analysis, beginning with functional mean estimation and extending to varying coefficient model estimation. Our work introduces a distributed learning framework involving multiple servers, each responsible for collecting several sp...

SimCortex v2: Pre-trained Models and Reproducibility Files

SimCortex v2 is a deep-learning framework for simultaneous reconstruction of left and right white-matter and pial cortical surfaces from T1-weighted MRI. This Zenodo record provides the finalized pretrained segmentation and deformation model weights, frozen training/inference/evaluation configurations, the HCP-YA/OASIS...

shailinsta/ZT-TRUSTFED: v1.0.2

ZT-TrustFed: A Trust-Aware Federated Intrusion Detection Framework for Heterogeneous and Non-IID Security Data ZT-TrustFed is a trust-based federated learning system for intrusion detection that integrates: FedProx regularization Dynamic trust-based weighted aggregation (T-Krum) Gaussian differential privacy Single-ste...

HuddsTrafficFL: A Benchmark Dataset for Byzantine-Robust Federated Learning in Urban Traffic Systems

HuddsTrafficFL is a benchmark dataset for Byzantine-robust federated learning research in urban traffic systems. Built from a city-scale SUMO/TraCI simulation of Huddersfield's road network using real OpenStreetMap data and validated against UK Department for Transport traffic counts for Kirklees borough. The dataset i...

Federated learning guided by model performance: a new aggregation strategy

ZeroTrust-FL-Sim

A reproducible zero-trust federated-learning security and resilience simulation platform covering non-IID data, malicious clients, Byzantine-robust aggregation, differential privacy, CKKS aggregation, post-quantum transport, acceleration, chaos engineering, and observability.

Quantum Federated Digital Twin Framework with Explainable Multi-Agent Intelligence for Autonomous Engineering Management