21 citations · 53 across the 14 of their papers we have counts for
8 papers · 1 filter
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering
Rui Wang, Xingkai Wang, Huanhuan Chen +4
Byzantine-robust Federated Learning (FL) aims to counter malicious clients and train an accurate global model while maintaining an extremely low attack success rate. Most existing…
ID-based self-encryption via Hyperledger Fabric based smart contract
Ilya Grishkov, Roland Kromes, Thanassis Giannetsos +1
This paper offers a prototype of a Hyperledger Fabric-IPFS based network architecture including a smart contract based encryption scheme that meant to improve the security of user'…
Combining ID's, Attributes, and Policies in Hyperledger Fabric
Daan Gordijn, Roland Kromes, Thanassis Giannetsos +1
This work aims to provide a more secure access control in Hyperledger Fabric blockchain by combining multiple ID's, attributes, and policies with the components that regulate acces…
Effect of Homomorphic Encryption on the Performance of Training Federated Learning Generative Adversarial Networks
Ignjat Pejic, Rui Wang, Kaitai Liang
A Generative Adversarial Network (GAN) is a deep-learning generative model in the field of Machine Learning (ML) that involves training two Neural Networks (NN) using a sizable dat…
FLVoogd: Robust And Privacy Preserving Federated Learning
Yuhang Tian, Rui Wang, Yanqi Qiao +2
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particu…
Using Autoencoders on Differentially Private Federated Learning GANs
Gregor Schram, Rui Wang, Kaitai Liang
Machine learning has been applied to almost all fields of computer science over the past decades. The introduction of GANs allowed for new possibilities in fields of medical resear…
MULTI-FLGANs: Multi-Distributed Adversarial Networks for Non-IID distribution
Akash Amalan, Rui Wang, Yanqi Qiao +2
Federated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while pres…
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
Jing Xu, Rui Wang, Stefanos Koffas +2
Graph Neural Networks (GNNs) are a class of deep learning-based methods for processing graph domain information. GNNs have recently become a widely used graph analysis method due t…