Latest Articles for Federated Learning

DBFed aims to reduce bias in AI while maintaining data privacy.

Examining pretrained transformers for multitask learning and communication efficiency in federated settings.

Fed-CPrompt enhances federated continual learning while preserving user privacy.

Discover how federated learning enhances privacy while improving machine learning efficiency.

A new approach to improve Federated Learning efficiency and model performance.

A framework that improves federated learning for mobile devices, enhancing privacy and efficiency.

Federated learning enhances AI-generated content while addressing privacy and efficiency.

New library enhances dataset creation for machine learning research.

Integrating explainable AI into 6G enhances user trust and performance.

FedBug tackles client drift while enhancing federated learning efficiency and privacy.

A new method for improved fairness in federated learning client selection.

New methods improve model training efficiency and privacy in deep learning.

Examining how to protect personal health data in metaverse healthcare systems.

Innovative approaches enhance data privacy and model performance in connected devices.

Exploring AI's role in managing complex 6G network demands.

FedDRL enhances federated learning by focusing on model quality and security.

EdgeConvEns improves deep learning while keeping data private and secure.

A new approach to improve vehicle communication and data security.

A look at splitfed learning and its benefits for IoT devices.

Federated Learning enables secure model training without exposing personal data.

Federated Learning enhances statistical accuracy while protecting individual privacy in data collection.

A fresh approach to protect privacy in time series data analysis.

AQUILA enhances federated learning by optimizing device selection and data communication.

A new method improves federated learning while preserving patient data privacy.

FPGAs improve the efficiency and safety of Federated Learning processes.

A new method improves privacy and model training in federated learning.

New framework improves QNN training with encrypted data and privacy protection.

Examining the challenges and solutions for connected and autonomous vehicle operations.

New method reveals vulnerabilities in Vertical Federated Learning systems using Graph Neural Networks.

Federated learning enhances privacy while improving model training on mobile devices.

A new method improves survival analysis while protecting patient privacy.

Federated learning allows secure collaboration while keeping sensitive data private.

A new method improves meeting summaries while protecting sensitive data.

Federated Learning enables data privacy while improving machine learning collaboration among diverse participants.

Explore an innovative defense method to enhance federated learning security.

New methods to secure federated learning against attacks while preserving user privacy.

A new method improves segmentation of organs and tumors in medical images.

This research focuses on improving efficiency and privacy in federated learning through adaptive compression methods.

Learn how PPSR protects data privacy in symbolic regression tasks.

FedIns tackles data challenges in federated learning to improve model performance.