PPGAN: Privacy-preserving Generative Adversarial Network

no code implementations • 4 Oct 2019 • Yi Liu, Jialiang Peng, James J. Q. Yu, Yi Wu

To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure.

Generative Adversarial Network Privacy Preserving

A Secure Federated Learning Framework for 5G Networks

no code implementations • 12 May 2020 • Yi Liu, Jialiang Peng, Jiawen Kang, Abdullah M. Iliyasu, Dusit Niyato, Ahmed A. Abd El-Latif

In this article, we propose a blockchain-based secure FL framework to create smart contracts and prevent malicious or unreliable participants from involving in FL.

Federated Learning

Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

no code implementations • 8 Dec 2020 • Yi Liu, Ruihui Zhao, Jiawen Kang, Abdulsalam Yassine, Dusit Niyato, Jialiang Peng

Second, we propose an asynchronous local differential privacy mechanism, which improves communication efficiency and mitigates gradient leakage attacks by adding well-designed noise to the gradients of edge nodes.

Edge-computing

FedBA: Non-IID Federated Learning Framework in UAV Networks

no code implementations • 10 Oct 2022 • Pei Li, Zhijun Liu, Luyi Chang, Jialiang Peng, Yi Wu

This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones.

Federated Learning