21 citations · 67 across the 13 of their papers we have counts for
6 papers · 1 filter
Federated Learning on Non-iid Data via Local and Global Distillation
Xiaolin Zheng, Senci Ying, Fei Zheng +4
Most existing federated learning algorithms are based on the vanilla FedAvg scheme. However, with the increase of data complexity and the number of model parameters, the amount of…
Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios
Haochen Mei, Gaolei Li, Jun Wu +1
Federated learning (FL) naturally faces the problem of data heterogeneity in real-world scenarios, but this is often overlooked by studies on FL security and privacy. On the one ha…
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
Jun Zhou, Longfei Zheng, Chaochao Chen +6
Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has b…
ASFGNN: Automated Separated-Federated Graph Neural Network
Longfei Zheng, Jun Zhou, Chaochao Chen +3
Graph Neural Networks (GNNs) have achieved remarkable performance by taking advantage of graph data. The success of GNN models always depends on rich features and adjacent relation…
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen, Jun Zhou, Longfei Zheng +7
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between…
Industrial Scale Privacy Preserving Deep Neural Network
Longfei Zheng, Chaochao Chen, Yingting Liu +6
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction. Meanwhile, data isolation has become…