Publications

Achieving Violation-Free Distributed Optimization under Coupling Constraints
Changxin Liu, Xiao Tan, Xuyang Wu, Dimos V. Dimarogonas and Karl H. Johansson. Submitted.

Enhancing Privacy in Federated Learning through Local Training
Nicola Bastianello, Changxin Liu and Karl H. Johansson. Submitted.

Federated Cubic Regularized Newton Learning with Sparsification-Amplified Differential Privacy
Wei Huo, Changxin Liu, Kemi Ding, Karl H. Johansson and Ling Shi. Accepted to Automatica, 2025.

Asynchronous Distributed Optimization with Delay-Free Parameters
Xuyang Wu, Changxin Liu, Sindri Magnusson and Mikael Johansson. Accepted to IEEE Transactions on Automatic Control, 2025.

Distributed Model Predictive Control for Optimal Output Consensus of Multi-Agent Systems over Directed Graphs
Nan Bai, Qishao Wang, Zhisheng Duan and Changxin Liu. Automatica, 178, 112381, 2025.

A Continuous-Time Violation-Free Multi-Agent Optimization Algorithm and its Applications to Safe Distributed Control
Xiao Tan, Changxin Liu, Dimos V. Dimarogonas and Karl H. Johansson. IEEE Transactions on Automatic Control, 70(8): 5176-5189, 2025.

Constrained Optimization with Decision-Dependent Distributions
Zifan Wang, Changxin Liu, Thomas Parisini, Michael M. Zavlanos and Karl H. Johansson. IEEE Transactions on Automatic Control, 70(8): 5114-5128, 2025.

A Robust Distributed MPC Framework for Multi-Agent Consensus with Communication Delays
Henglai Wei, Changxin Liu and Yang Shi. IEEE Transactions on Automatic Control, 69(11): 7418-7432, 2024.

Distributed Empirical Risk Minimization with Differential Privacy
Changxin Liu, Karl H. Johansson and Yang Shi. Automatica, 162, 111514, 2023.

Decentralized Composite Optimization in Stochastic Networks: A Dual Averaging Approach with Linear Convergence
Changxin Liu, Zirui Zhou, Jian Pei, Yong Zhang and Yang Shi. IEEE Transactions on Automatic Control, 68(8): 4650-4665, 2022.

Self-Triggered Adaptive Model Predictive Control of Constrained Nonlinear Systems: A Min-Max Approach
Kunwu Zhang, Changxin Liu and Yang Shi. Automatica, 142, 110424, 2022.

Accelerated Dual Averaging Methods for Decentralized Constrained Optimization
Changxin Liu, Yang Shi, Huiping Li and Wenli Du. IEEE Transactions on Automatic Control, 68(4): 2125-2139, 2022.

Stabilizing Terminal Constraint-Free Nonlinear MPC via Sliding Mode-Based Terminal Cost
Daxiong Ji, Jie Ren, Changxin Liu and Yang Shi. Automatica, 134, 109898, 2021.

Resource-Aware Exact Decentralized Optimization Using Event-Triggered Broadcasting
Changxin Liu, Huiping Li and Yang Shi. IEEE Transactions on Automatic Control, 66(7): 2961-2974, 2021.

A Unitary Distributed Subgradient Method for Multi-Agent Optimization with Different Coupling Sources
Changxin Liu, Huiping Li and Yang Shi. Automatica, 114, 108834, 2020.

Distributed Event-Triggered Gradient Method for Constrained Convex Minimization
Changxin Liu, Huiping Li, Yang Shi and Demin Xu. IEEE Transactions on Automatic Control, 65(2): 778-785, 2020.

Co-Design of Event Trigger and Feedback Policy in Robust Model Predictive Control
Changxin Liu, Huiping Li, Yang Shi and Demin Xu. IEEE Transactions on Automatic Control, 65(1): 302-309, 2020.

Robust Self-Triggered Min-Max Model Predictive Control for Discrete-Time Nonlinear Systems
Changxin Liu, Huiping Li, Jian Gao and Demin Xu. Automatica, 89: 333-339, 2018.

Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
Yuhao Yi, Ronghui You, Hong Liu, Changxin Liu, Yuan Wang and Jiancheng Lv. Accepted to Proceedings of the 38th Annual AAAI Conference on Artificial Intelligence (AAAI), 2024.

Delay-Agnostic Asynchronous Coordinate Update Algorithm
Xuyang Wu, Changxin Liu, Sindri Magnusson and Mikael Johansson. In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.

Improving Fairness for Data Valuation in Horizontal Federated Learning
Zhenan Fan, Huang Fang, Zirui Zhou, Jian Pei, Michael P. Friedlander, Changxin Liu and Yong Zhang. In Proceedings of the 38th IEEE International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, May 9-12, 2022.