Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model Using Subspace Perturbation-Reference-Cited by-同舟云学术

Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model Using Subspace Perturbation

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
language:
Short-container-title:

Author:

Li Qiongxiu¹,Gundersen Jaron Skovsted²,Tjell Katrine²,Wisniewski Rafal²,Christensen Mads Grasboll¹

Affiliation:

1. Aalborg University,Audio Analysis Lab, CREATE,Denmark

2. Aalborg University,Department of Electronic Systems,Denmark

Publisher

IEEE

Link

Reference31 articles.

1. Non-parametric entropy estimation toolbox (npeet);ver steeg,2000

2. UCI machine learning repository;dua,2017

Cited by 7 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

3. On the Privacy of Federated Clustering: a Cryptographic View;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. Topology-Dependent Privacy Bound for Decentralized Federated Learning;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

5. Nys-FL: A communication efficient Federated learning with Nyström approximated Global Newton direction;2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2023-12-17