Latest Articles for Federated Learning

Researchers can study health conditions without accessing individual patient records.

This article explores a new method for better merging of machine learning models.

A new framework improves audio classification by leveraging multi-modal device knowledge.

An innovative approach to enhance resource management in IoT systems.

Celtibero provides strong protection against attacks in federated learning systems.

A look at federated analysis and privacy technologies for sensitive data.

Explore federated learning's approach to privacy in machine learning.

Discover how Federated Learning keeps data private while enabling collaboration in IIoT.

Research focuses on improving accuracy and efficiency in radio signal positioning.

A new method enhances federated learning's security by combating Byzantine attacks effectively.

Exploring Secure Aggregation's role in Federated Learning within healthcare settings.

RoLoRA enhances federated learning with robust fine-tuning and efficient communication.

A fresh approach to improve model accuracy by identifying low-quality data sources.

Exploring blockchain technology for secure model training in robot swarms.

New approach uses federated learning to optimize mobile task offloading decisions.

CoAst offers innovative contribution assessment in federated learning without needing validation data.

Introducing FedFT, a method to improve communication in Federated Learning.

A web app simplifying federated learning for enhanced data privacy and user accessibility.

A method to enhance collaboration while protecting sensitive data in silos.

A new framework combines federated learning and multimodal models to improve privacy and performance.

RC-FED reduces communication costs while maintaining model quality in federated learning.

A new method enhances federated learning by addressing diverse data challenges.

A new method improves brain tumor diagnosis while protecting patient privacy.

Innovative synthetic data method improves medical imaging accuracy while protecting patient privacy.

This research focuses on training ControlNet while safeguarding user data across devices.

Exploring efficient algorithms for decentralized learning while preserving data privacy.

FedD2P offers a new way for IoT to train models without heavy resources.

Learn how PQC strengthens federated learning against quantum threats.

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.