Qiongxiu Li

This study examines privacy differences between decentralized and centralized federated learning.

A look at privacy concerns in centralized and decentralized learning systems.

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

A new method protects privacy while finding maximum values in distributed networks.

ProP offers an effective way to catch backdoor attacks on machine learning models.