Latest Articles for Federated Learning

EdgeFL simplifies federated learning while ensuring data privacy and efficiency.

A new method using federated learning for large-scale mapping with neural radiance fields.

New methods improve model training while protecting user data privacy.

A method to address privacy and fairness concerns in machine learning.

A novel method that uses TEEs to protect machine learning models in federated learning.

Learning from Drift improves model performance in federated learning with diverse data.

FedDIP optimizes communication in federated learning through dynamic pruning and regularization.

FedJudge offers a privacy-focused approach to legal language model training.

Introducing XFedHunter for advanced detection and analysis of APT attacks in networks.

A look at challenges in Federated Learning from data reconstruction attacks.

Exploring ways to improve data sharing while ensuring user privacy.

FedGKD enhances federated learning for Graph Neural Networks by optimizing task feature extraction.

Explore how agents work together for better solutions in complex tasks.

Exploring federated learning techniques to enhance communication efficiency while preserving user privacy.

Managing EV charging to stabilize the power grid while ensuring driver privacy.

Fed-LSAE boosts security in federated learning and counters poisoning attacks.

AI's role in transforming network management for advanced wireless technologies.

A new method enhances Federated Learning by addressing staleness and device differences.

A new approach enhances federated learning for diverse client data.

A new method enhances model accuracy through federated learning and kernel regression.

A look at Federated Deep Equilibrium Learning and its impact on AI.

A guide to collaborative distributed machine learning while maintaining data privacy.

Analyzing federated and consensus methods for prostate segmentation from MRI scans.

A new approach to boost machine learning efficiency in satellite communication.

A new approach secures participant selection in federated learning to protect privacy.

This paper presents a method to improve fairness in federated learning while safeguarding data privacy.

A new approach to improve Federated Learning amidst uneven user participation.

Brave offers privacy and protection from threats in peer-to-peer federated learning.

A look into Federated Learning and Unlearning for data privacy.

AdaFed improves fairness in federated learning while maintaining model accuracy.

Improving model training efficiency and privacy in federated learning through coreset selection.

A method to enhance translation quality while maintaining privacy in multilingual communication.

A method to evaluate treatments while protecting individual privacy.

A new method enhances federated learning by reducing communication burdens and tackling client drift.

A new framework improves machine learning models for uneven data distributions.

Examining the risks of integrating Foundation Models into Federated Learning systems.

A new method enhances federated learning efficiency while maintaining data privacy.

Deep learning enhances accuracy in detecting colorectal cancer polyps.

FedGT improves federated learning with enhanced privacy and performance for graph data.

A new approach enhances satellite data processing using federated learning methods.