Selected Publications

Journals 

• Wenrui Yu*, Qiongxiu Li*, Milan Lopuhaä-Zwakenberg, Mads Græsbøll Christensen, Richard Heusdens."Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization" , IEEE. Trans. on Information Forensics and Security, 2024, [Link]

• Xiao Li, Qiongxiu Li, Zhanhao Hu, and Xiaolin Hu. " On the Privacy Effect of Data Enhancement via the Lens of Memorization", IEEE. Trans. on Information Forensics and Security, 2023, [Link]

• Qiongxiu Li, Jaron Skovsted Gundersen, Milan Lopuhaa-Zwakenberg, and Richard Heusdens. "Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus." IEEE. Trans. on Information Forensics and Security, 2023, [Link]

• Qiongxiu Li, Richard Heusdens, and Mads Græsbøll Christensen. "Communication Efficient Privacy-Preserving Distributed Optimization using Adaptive Differential Quantization." Signal Processing, 2022,  [link] 

• Qiongxiu Li, Jaron Skovsted Gundersen, Richard Heusdens, and Mads Græsbøll Christensen. "Privacy-Preserving Distributed Processing: Metrics, Bounds, and Algorithms."  IEEE. Trans. on Information Forensics and Security, 2021,  [Link] 

•  Qiongxiu Li, Richard Heusdens, and Mads Græsbøll Christensen. "Privacy-Preserving Distributed optimization via Subspace Perturbation: A General Framework."  IEEE. Trans. on Signal Processing, 2020,  [link] 

Conferences

• Qiongxiu Li, Milan Lopuhaä-Zwakenberg, Wenrui Yu, Richard Heusdens. "On the Privacy Bound of Distributed Optimization and its Application in Federated Learning", EUSIPCO 2024

• Qiongxiu Li, Wenrui Yu, Changlong Ji, and Richard Heusdens. "Topology-dependent Privacy Bound for Decentralized Federated Learning", ICASSP, 2024

• Qiongxiu Li, and Lixia Luo. "On the Privacy of Federated Clustering: A Cryptographic View", ICASSP, 2024

• Sebastian O. Jordan, Qiongxiu Li, and Richard Heusdens. “Privacy-Preserving Distributed Optimisation Using Stochastic PDMM”, ICASSP, 2024

• Qiongxiu Li, Jaron Skovsted Gundersen, Katrine Tjell, Rafal Wisniewski, and Mads Græsbøll Christensen. "Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture model using subspace perturbation", ICASSP, 2022.

• Qiongxiu Li, Milan Lopuhaa-Zwakenberg, Richard Heusdens, and Mads Græsbøll Christensen."Two for the price of one: communication efficient and privacy-preserving distributed average consensus using quantization", EUSIPCO, 2022. 

• Qiongxiu Li, Richard Heusdens, and Mads Græsbøll Christensen. "Privacy-Preserving Distributed Least Squares via Subspace Perturbation." EUSIPCO, 2020. 

• Qiongxiu Li, Mario Coutino, Geert Leus  and Mads Græsbøll Christensen. "Privacy-Preserving Distributed Graph Filtering." EUSIPCO, 2020.

• Qiongxiu Li, Richard Heusdens, and Mads Græsbøll Christensen. "Covex Optimisation-based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks." ICASSP, 2020. 

• Qiongxiu Li, Ignacio Cascudo, and Mads Græsbøll Christensen. "Privacy-Preserving Distributed Average Consensus based on Additive Secret Sharing."  EUSIPCO, 2019.

• Qiongxiu Li, and Mads Græsbøll Christensen. "A Privacy-Preserving Asynchronous Averaging Algorithm based on Shamir’s Secret Sharing." EUSIPCO, 2019.