Submitted Paper

  1. Jiaojiao Zhang, Jiang Hu, Mikael Johansson. Non-convex composite federated learning with heterogeneous data. arXiv preprint arXiv:2502.03958.

Journal Paper

  1. Jiaojiao Zhang, Linglingzhi Zhu, Dominik Fay, Mikael Johansson. Locally Differentially Private Online Federated Learning With Correlated Noise, 2025. Accepted by IEEE Transactions on Signal Processing.

  2. Jiang Hu, Jiaojiao Zhang, and Kangkang Deng. Achieving local consensus over compact submanifolds. IEEE Transactions on Automatic Control, 2025.

  3. Erik Berglund, Jiaojiao Zhang, Mikael Johansson. Soft quasi-Newton: Guaranteed positive definiteness by relaxing the secant constraint, 2025. Accepted by Optimization Methods and Software. (I am the corresponding author)

  4. Jiaojiao Zhang, Xuechao He, Yue Huang, Qing Ling. Byzantine-Robust and Communication-Efficient Personalized Federated Learning. IEEE Transactions on Signal Processing, 2024.

  5. Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So, Qing Ling. Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning. IEEE Transactions on Signal Processing, 2023.

  6. Huikang Liu, Jiaojiao Zhang, Anthony Man-Cho So, Qing Ling. A Communication-Efficient Decentralized Newton’s Method with Provably Faster Convergence. IEEE Transactions on Signal and Information Processing over Networks, 2023. (I am the corresponding author)

  7. Jiaojiao Zhang, Huikang Liu, Anthony Man-Cho So, Qing Ling. A Penalty Alternating Direction Method of Multipliers for Convex Composite Optimization over Decentralized Networks. IEEE Transactions on Signal Processing, 2021.

  8. Jiaojiao Zhang, Qing Ling, Anthony Man-Cho So. A Newton Tracking Algorithm with Exact Linear Convergence for Decentralized Consensus Optimization. IEEE Transactions on Signal and Information Processing over Networks, 2021.

  9. Jiaojiao Zhang, Shuang Cong, Qing Ling, Kezhi Li and Herschel Rabitz. Quantum State Filter with Disturbance and Noise. IEEE Transactions on Automatic Control, 2019.

  10. Jiaojiao Zhang, Shuang Cong, Qing Ling, Kezhi Li. An Efficient and Fast Quantum State Estimator with Sparse Disturbance. IEEE Transactions on Cybernetics, 2018.

  11. Jiaojiao Zhang, Kezhi Li, Shuang Cong. Efficient Reconstruction of Density Matrices for High Dimensional Quantum State Tomography. Signal Processing, 2017.

Conference Paper

  1. Yue Huang, Jiaojiao Zhang, Qing Ling. Differential privacy in distributed learning: Beyond uniformly bounded stochastic gradients. Accepted by Artificial Intelligence and Statistics, AISTATS 2025.

  2. Zesen Wang, Jiaojiao Zhang, Xuyang Wu, Mikael Johansson. From promise to practice: realizing high-performance decentralized training. Accepted by The Thirteenth International Conference on Learning Representations, ICLR 2025.

  3. Jiaojiao Zhang, Jiang Hu, Mikael Johansson. Composite Federated Learning with Heterogeneous Data. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024. (The Best Paper Award, 1/2800)

  4. Jiaojiao Zhang, Jiang Hu, Anthony Man-Cho So, Mikael Johansson. Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. Accepted by the 38th Annual Conference on Neural Information Processing Systems, NeurIPS 2024.

  5. Jiaojiao Zhang, Dominik Fay, Mikael Johansson. Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2024.

  6. Jiaojiao Zhang, Linglingzhi Zhu, Mikael Johansson. Differentially Private Online Federated Learning with Correlated Noise. Accepted by IEEE Conference on Decision and Control, CDC 2024.