Latest Articles for Federated Learning

QuanCrypt-FL enhances security in Federated Learning using advanced techniques.

This system enhances DR detection while maintaining patient privacy.

This research highlights innovative methods for privacy in collaborative machine learning.

New technologies enhance diagnosis and treatment of intracranial hemorrhages.

Federated learning enhances medical imaging while protecting patient privacy.

A method to enhance Federated Learning by adjusting client contributions for better outcomes.

A new defense strategy enhances model privacy without sacrificing performance.

ClipFL enhances federated learning by removing noisy devices for better performance.

Combining federated learning and GNNs for improved stroke assessment while ensuring patient privacy.

A new method blends efficiency and accuracy in Federated Learning.

This article reviews strategies for improving deep learning in diverse medical image settings.

Addressing privacy and fairness in machine learning through innovative methods.

A guide on fair resource distribution in diverse groups.

Discover how Federated Learning addresses data privacy in connected devices.

FedRewind improves collaboration among nodes in federated learning while maintaining data privacy.

Learn how devices train themselves while keeping your data safe.

A plugin safeguards federated learning models from harmful updates without compromising patient privacy.

A new approach to assess pancreatic growths while protecting patient privacy.

New method improves skin lesion classification while protecting patient data.

Learn how UAVs change the game for wireless communication.

Movable antennas enhance data sharing while protecting privacy in federated learning.

A new method allows safe data analysis for healthcare studies.

Explore federated learning, a method for training models without sharing personal data.

Assessing vulnerabilities in federated learning's privacy through attribute inference attacks.

Exploring the intersection of Auto-ML and Federated Learning for better data privacy.

Discover how federated learning enhances activity recognition while addressing data challenges.

A new method improves traffic prediction and reduces congestion.

A new approach to predicting energy needs while keeping data private.

FedRAV allows autonomous vehicles to learn collaboratively while keeping data private.

New methods tackle challenges in identifying weak classes within federated learning.

A look at fairness challenges in Federated Learning and the WassFFed framework.

A new approach to enhance robot learning while protecting privacy.

A new framework improves time series forecasting while ensuring data privacy.

CAMA enhances federated learning efficiency while reducing energy consumption and emissions.

eFedLLM paves the way for easier AI use.

A look at how FedAlign enhances learning without compromising data privacy.

FOGSense offers innovative detection for Freezing of Gait in everyday life.

A look at domain adaptation, privacy, and federated learning in data science.

FedGR improves federated learning by refining noisy labels for better collaboration.

Federated learning protects brain data while improving motor imagery classification.