Latest Articles for Federated Learning

A look at how MP-SL aids devices in machine learning while ensuring privacy.

Exploring ways to enhance trustworthiness in distributed artificial intelligence technologies.

A new approach combines energy harvesting with federated learning for better battery management.

Discover how DFML transforms data learning without central servers.

Integrating Foundation Models with Federated Learning presents both risks and benefits.

A strategy to enhance performance and fairness in federated learning models.

Enhancements in federated learning improve efficiency and privacy for IoT applications.

This research examines vulnerabilities in SNNs combined with federated learning techniques.

New strategies in Federated Learning enhance privacy and efficiency in machine learning.

Exploring how SFL can transform data processing in future mobile networks.

A new method improves model accuracy by addressing noisy labels in federated learning.

This study examines a method to enhance disease detection using multimodal data.

Flashback addresses forgetting in Federated Learning for improved model training and performance.

A new method enhances communication efficiency in federated learning.

Examining the relationship between data protection laws and machine learning practices.

Learn how P3LS enables safe data sharing in manufacturing.

hFedF improves federated learning performance by tackling domain generalization challenges.

Exploring fairness issues in machine learning and federated systems.

A novel method boosts model performance in federated learning while protecting privacy.

Combining Federated Learning with privacy techniques protects sensitive data while training models.

New methods in federated learning protect against attacks while maintaining data privacy.

A new secure approach to managing healthcare data using AI.

HFRec provides secure personalized course suggestions based on diverse student needs.

A new approach for secure anomaly detection in graph data.

Exploring the integration of digital twins and IoBNT in biotechnology.

CoDream enables organizations to collaborate securely without sharing sensitive data.

Discover how ESFL improves machine learning efficiency while protecting privacy.

FedReview improves federated learning by rejecting harmful model updates.

FedUV improves model performance in federated learning on non-IID data.

AerisAI enhances AI collaboration while protecting data privacy through decentralized methods.

New algorithms enhance privacy and accuracy in sparse data scenarios.

A new method combines federated learning and secure computation to protect gaze data privacy.

Examining privacy dangers in Federated Learning and large language models.

Asyn2F improves asynchronous federated learning for better model training and data privacy.

A new method enhances federated learning efficiency using client update strategies.

Examining federated unlearning and its challenges in machine learning privacy.

A look into the risks of data poisoning in federated learning systems.

Exploring privacy-preserving techniques in machine learning and their significance.

A look at federated learning and its impact on business decision-making.

Federated learning improves face recognition while protecting user privacy.