PILE: Robust Privacy-Preserving Federated Learning Via Verifiable Perturbations-Reference-Cited by-同舟云学术

PILE: Robust Privacy-Preserving Federated Learning Via Verifiable Perturbations

Author:

Tang Xiangyun¹^ORCID,Shen Meng²^ORCID,Li Qi³^ORCID,Zhu Liehuang²^ORCID,Xue Tengfei⁴,Qu Qiang⁴^ORCID

Affiliation:

1. School of Information Engineering, Minzu University of China, Beijing, China

2. School of Cyberspace Security, Beijing Institute of Technology, Beijing, China

3. Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing, China

4. Blockchain Lab, Huawei Cloud Tech Co., Ltd., Shenzhen, China

Funder

National Key R&D Program of China

NSFC Projects

Beijing Nova Program

Beijing Natural Science Foundation

CCF-Huawei Populus Euphratica Blockchain Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

Reference60 articles.

1. SafetyNets: Verifiable execution of deep neural networks on an untrusted cloud;ghodsi;Proc Adv Neural Inf Process Syst,2017

2. Federated learning with non-IID data;zhao,2018

4. On the convergence of fedavg on non-IID data;li;Proc 8th Int Conf Learn Representations,2020

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献