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

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

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022

16 September 2022

Jaron Skovsted Gundersen

M. G. Christensen

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Papers citing "Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation"

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Title
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges Qiongxiu Li Wenrui Yu Yufei Xia Jun Pang FedML 197 5 0 10 Mar 2025
Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical StudyIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024 Changlong Ji Stephane Maag Richard Heusdens Qiongxiu Li FedML 156 3 0 21 Sep 2024
Privacy-Preserving Distributed Maximum Consensus Without Accuracy LossIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2024 Wenrui Yu Richard Heusdens Jun Pang Qiongxiu Li 175 3 0 16 Sep 2024
On the privacy of federated Clustering: A Cryptographic ViewIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 Qiongxiu Li Lixia Luo FedML 158 4 0 13 Dec 2023
Topology-Dependent Privacy Bound For Decentralized Federated LearningIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023 Qiongxiu Li Wenrui Yu Changlong Ji Richard Heusdens 175 3 0 13 Dec 2023
Improving the Utility of Differentially Private Clustering through Dynamical ProcessingPattern Recognition (Pattern Recogn.), 2023 Junyoung Byun Yujin Choi Jaewoo Lee 245 1 0 27 Apr 2023