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Qiongxiu Li
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2020 – today
- 2024
- [j5]Qiongxiu Li
, Jaron Skovsted Gundersen
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus. IEEE Trans. Inf. Forensics Secur. 19: 1780-1793 (2024) - [j4]Xiao Li
On the Privacy Effect of Data Enhancement via the Lens of Memorization. IEEE Trans. Inf. Forensics Secur. 19: 4686-4699 (2024) - [c13]Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Wenrui Yu, Richard Heusdens:
On the Privacy Bound of Distributed Optimization and its Application in Federated Learning. EUSIPCO 2024: 2232-2236 - [c12]Qiongxiu Li
On the Privacy of Federated Clustering: a Cryptographic View. ICASSP 2024: 4865-4869 - [c11]Qiongxiu Li
Topology-Dependent Privacy Bound for Decentralized Federated Learning. ICASSP 2024: 5940-5944 - [c10]Sebastian O. Jordan, Qiongxiu Li
Privacy-Preserving Distributed Optimisation using Stochastic PDMM. ICASSP 2024: 8571-8575 - [i14]Wenrui Yu
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization. CoRR abs/2407.09324 (2024) - [i13]Xiao Li, Wenxuan Sun, Huanran Chen, Qiongxiu Li, Yining Liu, Yingzhe He, Jie Shi, Xiaolin Hu:
ADBM: Adversarial diffusion bridge model for reliable adversarial purification. CoRR abs/2408.00315 (2024) - [i12]Xinran Wu, Hui Zou, Yidan Xing, Jingjing Qu, Qiongxiu Li, Renxia Xue, Xiaoming Fu:
An Intelligent Innovation Dataset on Scientific Research Outcomes. CoRR abs/2409.06936 (2024) - [i11]Wenrui Yu
Privacy-Preserving Distributed Maximum Consensus Without Accuracy Loss. CoRR abs/2409.10226 (2024) - [i10]Qiongxiu Li, Lixia Luo, Agnese Gini, Changlong Ji, Zhanhao Hu, Xiao Li, Chengfang Fang, Jie Shi, Xiaolin Hu:
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem. CoRR abs/2409.14260 (2024) - [i9]Changlong Ji, Stephane Maag, Richard Heusdens, Qiongxiu Li:
Re-Evaluating Privacy in Centralized and Decentralized Learning: An Information-Theoretical and Empirical Study. CoRR abs/2409.14261 (2024) - 2023
- [i8]Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaä-Zwakenberg, Richard Heusdens:
Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus. CoRR abs/2312.07947 (2023) - [i7]Qiongxiu Li, Wenrui Yu, Changlong Ji
Topology-Dependent Privacy Bound For Decentralized Federated Learning. CoRR abs/2312.07956 (2023) - [i6]Qiongxiu Li, Lixia Luo:
On the privacy of federated Clustering: A Cryptographic View. CoRR abs/2312.07992 (2023) - 2022
- [j3]Qiongxiu Li
Communication efficient privacy-preserving distributed optimization using adaptive differential quantization. Signal Process. 194: 108456 (2022) - [c9]Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Richard Heusdens, Mads Græsbøll Christensen:
Two for the price of one: communication efficient and privacy-preserving distributed average consensus using quantization. EUSIPCO 2022: 2166-2170 - [c8]Qiongxiu Li
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model Using Subspace Perturbation. ICASSP 2022: 4263-4267 - [i5]Xiao Li, Qiongxiu Li, Zhanhao Hu, Xiaolin Hu:
On the Privacy Effect of Data Enhancement via the Lens of Memorization. CoRR abs/2208.08270 (2022) - [i4]Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafal Wisniewski, Mads Græsbøll Christensen:
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation. CoRR abs/2209.07833 (2022) - 2021
- [j2]Qiongxiu Li
Privacy-Preserving Distributed Processing: Metrics, Bounds and Algorithms. IEEE Trans. Inf. Forensics Secur. 16: 2090-2103 (2021) - [i3]Qiongxiu Li, Richard Heusdens, Mads Græsbøll Christensen:
Communication efficient privacy-preserving distributed optimization using adaptive differential quantization. CoRR abs/2105.14416 (2021) - 2020
- [j1]Qiongxiu Li
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework. IEEE Trans. Signal Process. 68: 5983-5996 (2020) - [c7]Qiongxiu Li
Convex optimization-based Privacy-Preserving Distributed Least Squares via Subspace Perturbation. EUSIPCO 2020: 2110-2114 - [c6]Qiongxiu Li
Privacy-Preserving Distributed Graph Filtering. EUSIPCO 2020: 2155-2159 - [c5]Qiongxiu Li
Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks. ICASSP 2020: 5895-5899 - [i2]Qiongxiu Li, Richard Heusdens, Mads Græsbøll Christensen:
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework. CoRR abs/2004.13999 (2020) - [i1]Qiongxiu Li, Jaron Skovsted Gundersen, Richard Heusdens, Mads Græsbøll Christensen:
Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms. CoRR abs/2009.01098 (2020)
2010 – 2019
- 2019
- [c4]Qiongxiu Li
A Privacy-Preserving Asynchronous Averaging Algorithm based on Shamir's Secret Sharing. EUSIPCO 2019: 1-5 - [c3]Qiongxiu Li
Privacy-Preserving Distributed Average Consensus based on Additive Secret Sharing. EUSIPCO 2019: 1-5 - 2016
- [c2]Qiongxiu Li, Changlong Jin, Weonjin Kim, Jungmin Kim, Shengzhe Li, Hakil Kim:
Multi-feature based score fusion method for fingerprint recognition accuracy boosting. APSIPA 2016: 1-4 - [c1]Eunsoo Park, Weonjin Kim, Qiongxiu Li, Jungmin Kim, Hakil Kim:
Fingerprint Liveness Detection Using CNN Features of Random Sample Patches. BIOSIG 2016: 321-328
Coauthor Index
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last updated on 2024-11-07 21:33 CET by the dblp team
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