federated learning

Evidential Federated Learning for Robust Multimodal Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) at scale faces two coupled obstacles: conversational data is partitioned across organizations under privacy constraints, and emotion labels are costly, leaving most utterances unlabelled. Federated learning addresses the first, but standard aggregation trusts every client upda...

DrNoshina/Federated-Learning-for-Privacy-Preserving: Initial release

No description provided.

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...

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...

AI-SAFE FlightNet : A Next-Generation Artificial Intelligence and Automation Framework for Predictive Flight Safety, Aircraft Health Intelligence, and Ground Operations Coordination

Aviation safety improvement remains a continuous engineering challenge because modern aircraft are sensor-dense, software-intensive, and operationally interconnected, while health and operations data often remain fragmented across onboard, maintenance, dispatch, and ground systems. This fragmentation can delay weak-sig...

Uncertainty-Aware Dynamic Federated Vision Transformer for Skin Cancer Detection in Healthcare Environments

Initial public release of DynamicFedSkinCancerDetection. This release provides the implementation of the proposed Uncertainty-Aware Dynamic Federated Vision Transformer with Uncertainty-Guided Aggregation for skin cancer classification in a simulated federated learning environment. The implementation includes preproces...

Personalized Federated Learning Under Severe Statistical Heterogeneity: A Multi-Dataset Analysis of Accuracy, Tail Performance, and Client Fairness

DrNoshina/Federated-Learning-for-Privacy-Preserving: Initial release

No description provided.

An efficient federated learning method based on the proportional-integral-derivative mechanism for embedded devices

An efficient federated learning method based on the PID mechanism (FL-PID) to adaptively adjust clients’ local training rounds and aggregation weights, which effectively solves the client data and computational heterogeneity problems in FL.

A Secure Federated Learning Paradigm Based on Homomorphic Encryption and Distributed Trust

Robust Asynchronous Federated Learning with Non-Convex Client Objective: Simulations

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...