federated learning

vantage6

Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange. The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey. Vantage6 is complete...

FMRAG

FMRAG is a Federated Learning architecture that deploys the RAG at the client side to make the federated learning system context-aware for clients by retrieving client-related data during the learning process instead of constructing a generic database at the server side, which reduces the accuracy for clients.

Ali-Forootani/Robust-Asynchronous-Federated-Learning: v1.0.1

No description provided.

Privacy-Preserving Federated Land-Cover Segmentation Under Non-IID Data: Region-Aware Contrastive Regularization and DP-SGD

Federated learning offers a practical route for collaborative land-cover mapping when satellite imagery cannot be pooled centrally, yet two issues remain: non-IID client data can weaken segmentation and shared model updates can still leak information. This study evaluates a privacy-preserving federated semantic-segment...

In-Vehicle Network Security: Securing the Internal Communication Network Within a Vehicle and Preventing Unauthorised Access to Critical Vehicle Systems

Abstract Modern vehicles are software-rich cyber-physical systems in which ECUs, zonal and central computing systems, sensors, diagnostic systems, and external interfaces share safety-critical and privacy-sensitive information. The evolution has created a larger attack surface for in-vehicle networks (IVNs), and widely...

In-Vehicle Network Security: Securing the Internal Communication Network Within a Vehicle and Preventing Unauthorised Access to Critical Vehicle Systems

Abstract Modern vehicles are software-rich cyber-physical systems in which ECUs, zonal and central computing systems, sensors, diagnostic systems, and external interfaces share safety-critical and privacy-sensitive information. The evolution has created a larger attack surface for in-vehicle networks (IVNs), and widely...

SFDT-DTVRA: Reproducibility Code for Closed-Loop Digital-Twin Validation of Federated

This software package contains the code used for the simulation, analysis, and reproducibility of a study on closed-loop digital-twin validation of federated learning updates for methane control in underground mining. The package includes the plant–twin simulator, federated-learning routines, calibration scripts, inter...

Client Clustering in Federated Learning: A Survey of Methods, Architectures, and Research Challenges

SFDT-DTVRA: Reproducibility Code for Closed-Loop Digital-Twin Validation of Federated

This software package contains the code used for the simulation, analysis, and reproducibility of a study on closed-loop digital-twin validation of federated learning updates for methane control in underground mining. The package includes the plant–twin simulator, federated-learning routines, calibration scripts, inter...

Data Trust Governance and Federated Learning in Healthcare: Contractual Mechanisms and Regulatory Compliance in Privacy-Preserving Data Ecosystems

Dataset for Proactive Fault Prediction in Industrial Internet of Things Using an Adaptive Federated Fog–Cloud Framework

This dataset contains multivariate industrial sensor data used for the evaluation of an adaptive federated fog–cloud framework for proactive fault prediction in Industrial Internet of Things (IIoT) environments. The dataset consists of real-world industrial time-series sensor measurements and is used to evaluate predic...

Adaptive Spam Detection: Leveraging Federated Learning for Privacy-Preserving Email Security

Email spam and spear phishing are and always will be a security and usability issue, and it is increasingly difficult to scale and protect centralized approaches that depend on aggregating users' mail data due to privacy and regulatory considerations (GDPR, CCPA, etc.). This paper formalizes the inherent tension betwee...