| A Newton tracking algorithm with exact linear convergence for decentralized consensus optimization J Zhang, Q Ling, AMC So IEEE Transactions on Signal and Information Processing over Networks 7, 346-358, 2021 | 34 | 2021 |
| Efficient reconstruction of density matrices for high dimensional quantum state tomography J Zhang, K Li, S Cong, H Wang Signal Processing 139, 136-142, 2017 | 21 | 2017 |
| A penalty alternating direction method of multipliers for convex composite optimization over decentralized networks J Zhang, H Liu, AMC So, Q Ling IEEE Transactions on Signal Processing 69, 4282-4295, 2021 | 18 | 2021 |
| An efficient and fast quantum state estimator with sparse disturbance J Zhang, S Cong, Q Ling, K Li IEEE transactions on cybernetics 49 (7), 2546-2555, 2018 | 16 | 2018 |
| Variance-reduced stochastic quasi-newton methods for decentralized learning J Zhang, H Liu, AMC So, Q Ling IEEE Transactions on Signal Processing 71, 311-326, 2023 | 15 | 2023 |
| Quantum state filter with disturbance and noise J Zhang, S Cong, Q Ling, K Li, H Rabitz IEEE Transactions on Automatic Control 65 (7), 2856-2866, 2019 | 12 | 2019 |
| A communication-efficient decentralized newton's method with provably faster convergence H Liu, J Zhang, AMC So, Q Ling IEEE Transactions on Signal and Information Processing over Networks, 2023 | 8 | 2023 |
| Dynamic privacy allocation for locally differentially private federated learning with composite objectives J Zhang, D Fay, M Johansson ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 4 | 2024 |
| Byzantine-robust and communication-efficient personalized federated learning X He, J Zhang, Q Ling ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023 | 4 | 2023 |
| Composite federated learning with heterogeneous data J Zhang, J Hu, M Johansson ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 3 | 2024 |
| Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data J Zhang, J Hu, AMC So, M Johansson arXiv preprint arXiv:2406.08465, 2024 | 2 | 2024 |
| Achieving consensus over compact submanifolds J Hu, J Zhang, K Deng arXiv preprint arXiv:2306.04769, 2023 | 2 | 2023 |
| Variance-reduced stochastic quasi-Newton methods for decentralized learning—Part II: Damped limited-memory DFP and BFGS methods J Zhang, H Liu, AMC So, Q Ling arXiv preprint arXiv:2201.07733, 2022 | 2 | 2022 |
| Variance-reduced stochastic quasi-Newton methods for decentralized learning—Part I: General framework J Zhang, H Liu, AMC So, Q Ling arXiv preprint arXiv:2201.07699, 2022 | 2 | 2022 |
| Differentially Private Online Federated Learning with Correlated Noise J Zhang, L Zhu, M Johansson arXiv preprint arXiv:2403.16542, 2024 | 1 | 2024 |
| Non-convex composite federated learning with heterogeneous data J Zhang, J Hu, M Johansson arXiv preprint arXiv:2502.03958, 2025 | | 2025 |
| Byzantine-Robust and Communication-Efficient Personalized Federated Learning J Zhang, X He, Y Huang, Q Ling IEEE Transactions on Signal Processing, 2024 | | 2024 |
| Locally Differentially Private Online Federated Learning With Correlated Noise J Zhang, L Zhu, D Fay, M Johansson arXiv preprint arXiv:2411.18752, 2024 | | 2024 |
| From promise to practice: realizing high-performance decentralized training Z Wang, J Zhang, X Wu, M Johansson arXiv preprint arXiv:2410.11998, 2024 | | 2024 |
Qing LingSchool of Computer Science and Engineering, Sun Yat-Sen UniversityE-mail megerősítve itt: mail.sysu.edu.cn
