Latest Articles for Federated Learning

A new approach to federated learning that improves accuracy and resource use.

Federated Learning tackles data privacy challenges in sensitive fields.

Federated Learning boosts privacy and efficiency in smart grid operations.

This article explores decentralized learning and the importance of similarity metrics.

A new method enhances data privacy in machine learning through personalized approaches.

Learn how data similarity improves agent collaboration in problem-solving.

A new method combines Federated Learning and Adversarial Training for insider threat detection.

Exploring how federated learning enhances AI efficiency and privacy.

This article discusses privacy-focused data methods using federated learning and deep transfer hashing.

A new approach enhances federated learning processes for better data transmission.

Companies face hidden privacy threats in federated learning due to data distribution shifts.

Combining Federated Learning and quantum technology promises safer data handling in AI.

Techniques to balance data distribution in federated learning for better model performance.

Examining the privacy challenges associated with Federated Learning and gradient inversion attacks.

This approach improves efficiency in training models from large graph data while protecting privacy.

A look into Hierarchical Federated Learning and its advantages for device privacy.

A new method enhances federated learning by reducing data transmission and energy usage.

A new method enhances gradient estimation while maintaining data privacy in federated learning.

A new method balances personalisation and privacy in federated learning.

Introducing a framework for secure training of machine learning models.

FeDETR uses federated learning for improved coronary stenosis evaluation.

Methods to improve privacy in federated Cox analysis are explored.

A-FedPD aligns model updates to reduce training instability in federated learning.

Exploring federated learning's impact on medical data privacy and protection strategies.

TAKFL optimizes knowledge sharing in federated learning for diverse device capabilities.

This paper enhances federated learning for GNNs while preserving user privacy.

This study enhances federated learning efficiency using edge servers for better resource management.

New methods improve data usage auditing in Federated Learning systems.

A new method to improve Federated Learning's resilience against data attacks.

Exploring Federated Learning's role in enhancing medical imaging while protecting patient privacy.

Combining global and local prompts enhances federated learning models while preserving data privacy.

A new model offers insights into job trends while protecting sensitive data.

A look at federated learning's challenges and solutions in maintaining data privacy and model accuracy.

Innovative strategies improve rare event detection in federated learning systems.

FLeNS improves federated learning by balancing learning speed and communication efficiency.

Examining privacy vulnerabilities in federated learning and their implications for data security.

AI enhances kidney stone detection while ensuring patient data privacy.

A look at how federated learning enhances patient care while maintaining privacy.

MASA offers a solution to enhance security in Federated Learning systems.

A new method to protect smart meters from FDI attacks while preserving privacy.