115 citations · 186 across the 11 of their papers we have counts for
6 papers · 1 filter
Distributed Learning for UAV Swarms
Chen Hu, Hanchi Ren, Jingjing Deng +1
Unmanned Aerial Vehicle (UAV) swarms are increasingly deployed in dynamic, data-rich environments for applications such as environmental monitoring and surveillance. These scenario…
Gradients Stand-in for Defending Deep Leakage in Federated Learning
H. Yi, H. Ren, C. Hu +3
Federated Learning (FL) has become a cornerstone of privacy protection, shifting the paradigm towards localizing sensitive data while only sending model gradients to a central serv…
An Element-Wise Weights Aggregation Method for Federated Learning
Yi Hu, Hanchi Ren, Chen Hu +2
Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the o…
A Survey on Vulnerability of Federated Learning: A Learning Algorithm Perspective
Xianghua Xie, Chen Hu, Hanchi Ren +1
This review paper takes a comprehensive look at malicious attacks against FL, categorizing them from new perspectives on attack origins and targets, and providing insights into the…
Gradient Leakage Defense with Key-Lock Module for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie
Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach where private data remains local, enabling secure computations and the exchange of local mo…
GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning
Hanchi Ren, Jingjing Deng, Xianghua Xie
Data privacy has become an increasingly important issue in Machine Learning (ML), where many approaches have been developed to tackle this challenge, e.g. cryptography (Homomorphic…