Research

Lei Wan, Jianxin Zhao, et al. “A Systematic Literature Review on Vehicular Collaborative Perception — A Computer Vision Perspective.” IEEE Transactions on Intelligent Transportation Systems, 2026. Preprint

F. Zhou, C. H. Liu, J. Zhao*, et al. “AoI and Latency-Aware Air-Ground Vehicular Crowdsensing by Sequential Multi-Agent Deep Reinforcement Learning.” IEEE TMC, 2026.

Q. Zhao, C. H. Liu, J. Zhao*, et al. “UAV Carrier Enabled Vehicular Crowdsensing by Multi-Agent Reinforcement Learning With Mutual Policy Divergence and Attentive Memory Update.” IEEE TMC, 2026.

H. Yang, C. H. Liu, G. Li, H. Wang, J. Zhao, et al. “Indoor Fingerprint Collection Under Environment Changes by Vehicular Crowdsensing: A Bayesian Reinforcement Learning Approach.” IEEE TMC, 2026.

X. Wu, S. Du, K. Qiu, S. Wen, H. Hou, Y. Liu, J. Zhao, et al. “TrustFCL: Task-grained Model Assessment and Aggregation for Secure Federated Continual Learning.” IJOCTA, 2026.

Z. Xia, J. Zhao, A. Vinel. “Dynamic-PBFT: Enhanced Consensus in Decentralized Federated Averaging for V2V Networks.” 2025 IEEE 102nd Vehicular Technology Conference (VTC2025-Fall). Link

X. Wu, J. Zhao, P. Xu, C. H. Liu. “Cross-Platform Deep Learning Compilation with Model-Specific Cost Model Pre-Training.” International Conference on Machine Learning and Computing (ICMLC’25).

J. Zhao, M-B. Lin, A. Vinel. “V2X-Based Decentralized Singular Value Decomposition in Dynamic Vehicular Environment.” 1st Workshop on Cooperative Intelligence for Embodied AI, ECCV 2024. Paper

Shuanqi Wang, Jianxin Zhao, Chi Liu, Wei Wu and Zhao Liu. “基于深度强化学习的二进制代码模糊测试方法” (Fuzz Testing Method of Binary Code Based on Deep Reinforcement Learning). 计算机科学, 2024. Paper

Jianxin Zhao, Pierre Vandenhove, Peng Xu, Hao Tao, Liang Wang, Chi Harold Liu, and Jon Crowcroft, “Parallel and Memory-Efficient Distributed Edge Learning in B5G IoT Networks”，IEEE Journal of Selected Topics in Signal Processing 17, no. 1 (2023): 222-233. Paper

Jianxin Zhao, Xinyu Chang, Yanhao Feng, Chi Harold Liu, and Ningbo Liu*, “Participant Selection for Federated Learning with Heterogeneous Data in Intelligent Transport System,” IEEE transactions on intelligent transportation systems 24, no. 1 (2023): 1106-1115. Paper

Jianxin Zhao, Yanhao Feng, Xinyu Chang, Peng Xu, Shilin Li, Chi Harold Liu, Wenke Yu, Jian Tang, and Jon Crowcroft, “Energy-Efficient and Fair IoT Data Distribution in Decentralised Federated Learning, ” IEEE Transactions on Network Science and Engineering 10, no. 3 (2023): 1352-1363. Paper

Shuang Li, Mixue Xie, Kaixiong Gong, Jianxin Zhao*, Chi Harold Liu, and Guoren Wang, “End-to-End Transferable Anomaly Detection via Multi-spectral Cross-domain Representation Alignment,” IEEE Transactions on Knowledge and Data Engineering, 2023. DOI: 10.1109/TKDE.2021.3118111. Paper

Wang Y, Wu J, Hua X, Liu CH, Li G, Zhao J, Yuan Y, Wang G. “Air-ground spatial crowdsourcing with uav carriers by geometric graph convolutional multi-agent deep reinforcement learning.” 2023 IEEE 39th International Conference on Data Engineering (ICDE) 2023 Apr 3 (pp. 1790-1802). IEEE. Paper

Ye, Y., Liu, C.H., Dai, Z., Zhao, J., Yuan, Y., Wang, G. and Tang, J., 2023, April. Exploring both individuality and cooperation for air-ground spatial crowdsourcing by multi-agent deep reinforcement learning. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) (pp. 205-217). IEEE. Paper

Jianxin Zhao*, Yanhao Feng, Xinyu Chang, and Chi Harold Liu, “Energy-efficient client selection in federated learning with heterogeneous data on edge,” Peer-to-Peer Networking and Applications 15, no. 2 (2022): 1139-1151. Paper

Jianxin Zhao, Rui Han, Yongkai Yang*, Benjamin Catterall, Chi Harold Liu, Lydia Y. Chen, Richard Mortier, Jon Crowcroft, and Liang Wang, “Federated Learning with Heterogeneity-Aware Probabilistic Synchronous Parallel on Edge,” IEEE Transactions on Services Computing 15, no. 2 (2022): 614-626. Paper

P. Xu, X. Chang, J. Zhao, C. H. Liu. “Automatic Operator Performance Tuning in a Machine Learning System on Edge.” ICPADS, 2022.

J. Zhao. “Executing Owl Computation on GPU and TPU.” OCaml 2019. Talk

J. Zhao, T. Tiplea, R. Mortier, J. Crowcroft, and L. Wang. “Data Analytics Service Composition and Deployment on Edge Devices.” Big-DAMA workshop, SIGCOMM’18 Paper

J. Zhao, R. Mortier, J. Crowcroft, and L. Wang. “Privacy-preserving Machine Learning Based Personal Data Analytics System.” AAAI/ACM conference on Artificial Intelligence, Ethics, and Society (AIES’18) Paper

J. Zhao, R. Mortier, J. Crowcroft, and L. Wang. “User-centric Composable Services for Personal Data Analytics.” SOSP 2017 (poster) Paper

J. Zhao. “Towards Security in Distributed Home System.” EuroSys’17 Doctoral Workshop Paper Slides

Richard Mortier et al. “Personal Data Management with the Databox: What’s Inside the Box?” CAN’16 Paper

Gao, H., Liu, C.H., Wang, W., Zhao, J., Song, Z., Su, X., Crowcroft, J. and Leung, K.K., 2015. A survey of incentive mechanisms for participatory sensing. IEEE Communications Surveys & Tutorials, 17(2), pp.918-943. Paper

Zhao, J., Liu, C. H., Chen, M., Liu, X., & Leung, K. K. (2015, June). Energy-efficient dynamic event detection by participatory sensing. In 2015 IEEE International Conference on Communications (ICC) (pp. 3180-3185). Paper