（一）研究方向：边缘智能系统、联邦学习、大模型与知识图谱推理

（二）在研科研项目：

（1）“十四五”国家重点研发课题：高坝大库系统巡检与自然灾害损害监测及场景构建

（2）科技研发项目：基于多无人载具协同的工程缺陷巡检技术研发及应用服务

（3）省重点研发计划：安全生产事故隐患智慧诊断关键技术与应用示范

（4）省科技成果转化项目—揭榜挂帅项目：自主C86高性能处理器与服务器系统的研发及产业化

（5）省重点研发计划：基于数字孪生的水电工程安全运行智慧管控模式

（6）国家电网公司科技项目：基于气象数据增强的大规模海上风电爬坡事件预测预警关键技术研究

（7）生态环保项目：基于无人机群的水库水环境监测技术研发及应用服务

（8）科技研发项目：基于多系统联动感知的水工运维外部应急风险智能响应研究

（三）实验室条件

实验室为同学开展科研工作提供了良好的实验环境：

（1） 配置了5台计算服务器，2*Nvidia A100 80G，2*Nvidia A800，4*Nvidia 3090 24G，以及远程独享使用8*海光DCU 32G，用于开展AI训练与推理任务。

（2）配置了若干Nvidia Jetson AGX Orin 64GB - Orin Nano 8GB系列开发板，用于边缘侧模型微调与推理工作。

2025年

1. Benteng Zhang, Yingchi Mao*, Xiaoming He, Huawei Huang, Jie Wu, Balancing Privacy and Accuracy using Significant Gradient Protection in Federated Learning, IEEE Trans. on Computers, Vol.74, No.1,Jan. 2025. (CCF-A) 

2. Yingchi Mao, Yi Rong*, Fudong Chi, Guangyu Li*, Haowen Xu, Ping Ping, Personalized Federated Learning over Edge-Cloud Collaborative Network for Intelligent Sensing Analysis, accepted by TSINGHUA SCIENCE AND TECHNOLOGY, 2025.01. (中科院一区) 

3. Yi Rong, Yingchi Mao, Xiaoming He, Mingkai Chen, Large-scale Traffic Flow Forecast with Lightweight LLM in Edge Intelligence, IEEE Internet of Things Magazine, 2025.01, early access

4. Yunzhe Jiang, Yinqiu Liu, Huajun Cui, Jinghuan Liu, Yingchi Mao, Xiaoming He, Traffic Prediction using Lightweight Large Model in UAV-assisted Mobile Computing for Time-critical Consumer Electronics, IEEE Trans. on Consumer Electronics, (中科院二区) 2025, early access

5. Zhenlong Dai, Bignrui Chen（my student）, Zhuoluo Zhao, et. al, Less is more: Adaptive Program Repair with Bug Localization and Preference Learning, accepted by AAAI 2025.  (CCF-A) 

6. Haowen Xu, Yingchi Mao*, Haotian Zheng, Xiaoming He, Yi Rong, Mingkai Chen, Saba AI-Rubaye, Energy-Efficient Personalized Federated Learning for Establishing Green IoT, accpeted by ICC 2025.  (CCF-C) 

7. Xiaoming He, Yunzhe Jiang, Yinqiu Liu, Huajun Cui, Heng Pan, and Yingchi Mao, Transforming 6G Mobile Edge Intelligence with Large Models, IEEE Networks, 2025.01, Early Access (JCR一区/中科院一区) 

8. Benteng Zhang, Yingchi Mao*, Haowen Xu, Yihan Chen, Muazu Tasiu, Xiaoming He, Jie Wu, Overcoming Forgetting Using Adaptive Federated Learning for IIoT Devices with Non-IID Data, accepted by IEEE Internet of Things Journal (IoTJ), 2025.02, (中科院1区, JCR Q1)

9. Benteng Zhang, Yingchi Mao*, Xiaoming He, Ping Ping, Huawei Huang, Jie Wu, Exploring the Privacy-Accuracy Trade-off Using Adaptive Gradient Clipping in Federated Learning, accepted by IEEE Trans. on Network Science and Engineering (TNSE), 2025.02, (中科院2区, JCR Q1)

10.  Benteng Zhang, Yingchi Mao*, Peng Zhang, Haotian Zheng, Xiaoming He, Jiawen Kang, Jie Wu, Aadpative Federated Learning for Large Models with Scarce and Non-IID Data in Cloud-Edge Networks, accepted by Infocom 2025 workshops on Integrating Edge Intelligence and Large Model in Next Generation Networks, 2025.02

2024年

1. Chang Li, Yingchi Mao*, Qian Huang, Xiaowei Zhu, Jie Wu, Scale-Aware Graph Convolutional Network with Part-Level Refinement for Skeleton-Based Human Action Recognition, IEEE Trans. on Circuits and Systems for Video Technology, June 2024, Vol. 34, No. 6, pp. 4311-4324.（CCF-B 中科院1区）10.1109/TCSVT.2023.3334872

2. Yingchi Mao, Lijuan Shen, Jun Wu*, Ping Ping, Jie Wu, Federated Dynamic Client Selection for Fairness Guarantee in Heterogeneous Edge Computing, Journal of Computer Science and Technology (JCST), 39(1): 139-158, 2024. DOI: 10.1007/s11390-023-2972-9 (CCF-B)

3. Rong Yi, Mao Yingchi*, Cui Huajun, He Xiaoming, Chen Mingkai*, Edge Computing Enabled Large-scale Traffic Flow Prediction with GPT in Intelligent Autonomous Transport System fir 6G Network, accepted by IEEE Transactions on Intelligent Transporation System (TITS), 2024.09 (中科院1区, JCR Q1)

4. Li Chang, Mao Yingchi*, Huang Qian, Xie Weiliang, He Xiaoming, Wu Jie, A Real-Time Emotion-Aware System Based on Wireless Body Area Network for IoMT Applications, accepted by IEEE Internet of Things Journal (IoTJ), 2024.09, (中科院1区, JCR Q1)

5. Xiao Jia, Yingchi Mao*, et al.  Few-shot Learning Based on Hierarchical Feature Fusion via Relation Networks, accepted by International Journal of Approximate Reasoning, 2024 (CCF-B)

6. Tasiu Muazu, Mao Yingchi*, et al, A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing, accepted by Computer Communication, 2024 (CCF-C JCR Q1)

7. Chang Li, Qian Huang, Yingchi Mao, Xing Li, Jie Wu, Multi-granular spatial-temporal synchronous graph convolutional network for robust action recognition, Expert Systems With Applications, 257 (2024) 124980. (中科院1区)

8. Hongliang Zhou, Yingchi Mao*, Xiang Li, Yi Rong*, Ling Chen, and Changkui Yin, TKSTAGNet: A Top-K Spatio-Temporal Attention Gating Network for Air Pollution Prediction, accepted by Expert Systems With Applications, 2024, (中科院1区)

9. Zhenxiang Pan, Yingchi Mao*, Li Xiong, Tianfu Pang, Ping Ping, MFAE: Multimodal Fusion and Alignment for Entity-level Disinformation Detection, accepted by Pattern Recognition Letters, 2024.06 (CCF-C)

10.  Haodong Cheng, Yingchi Mao*, Xiao Jia, A framework based on physics-informed graph neural ODE: for continuous spatial-temporal pandemic prediction, accepted by Applied Intelligence, 2024. (CCF-C, 中科院二区)

11. Li Xiong, Yingchi Mao*, Zicheng Wang, Chang Li, Cross-Modal Knowledge Learning with Scene Text for Fine-grained Image Classification, accepted by IET Image Processing, 2024 (CCF-C)

12. Yingchi Mao, Yi Rong*, Jiajun Wang, Zibo Wang, Xiaoming He, Jie Wu,Edge-Cloud Enabled Smart Sensing Applications with Personalized Federated Learning in IoT, accepted by 2024 GlobeComm (CCF-C) 

13. Yingchi Mao*, Zibo Wang, Chenxin Li, Jiakai Zhang, Shufang Xu and Jie Wu, Dual Adaptive Compression for Efficient Communication in Heterogeneous Federated Learning, accepted by the 24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid 2024,CCF-C)

14.  Wu Bo, Yingchi Mao*, Liu Yingjie, Ding Silong, Qi Rongzhi*, KRLGI: Knowlege Representation Learning Based on Global Information for Reasoning, accepted by the 36th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2024), 2024.10, CCF-C

15. Liu Yingjie, Yingchi Mao*, Wu Bo, Ding Silong, Qi Rongzhi*, LRIRL: Improving Knowledge Graph Reasoning through Representation Learning-Based Rule Induction, accepted by the 36th IEEE International Conference on Tools with Artifical Intelligence (ICTAI 2024), 2024.10, CCF-C

16. Hongle Guo, Yingchi Mao*, Xiaoming He, Benteng Zhang, Tianfu Pang, and Ping Ping, Improving Federated Learning through Abnormal Client Detection and Incentive, Computer Modeling in Engineering & Sciences, 2024, Vol. 139, no. 1

17. Xiao Jia, Yingchi Mao*, Ping Ping, Rongzhi Qi, Hierarchical Few-shot Learning Based on Top-down Correction Mechanism with Stop Strategy, accepted by International Journal of Machine Learning and Cybernetics, 2024. 06

18.  Yi Rong, Yingchi Mao*, Haowen Xu, Xiaoming He, Deep Reinforcement Learning Enabled UAVs Coverage Path Planning in Dam Inspection, accepted by the 26th IEEE International Conference on High Performance Computing and Communications (HPCC 2024), 2024.12, CCF-C

19.  Haotian Zheng, Yingchi Mao*, Haowen Xu, Xiaoming He, Jie Wu, FedMHC: Overcoming Dimensionality and Communication Chanllegens for Personalized Federated Learning Using Model Head Clustering, accpeted by the 26th IEEE International Conference on High Performance Computing and Communications (HPCC 2024), 2024.12, CCF-C

20. Abdoul Fatakhou Ba, Yingchi Mao, et al, ProLoRA: Resource-Efficient Personalized Federated Learning for Sensor Based Human Activity Recognition, in Proc. of the 20th International Conference on Mobility, Sensing, and Networking (MSN 2024),  2024.12, CCF-C

2023年

1 Xiaoming He, Yingchi Mao*, et, Channel Assignment and Power allocation for throughout improvement with PPO in B5G heterogeneous edge networks, Digital Communications and Networks, 2023,03 (SCI JCR Q1)

2 Tasiu Muazu, Mao Yingchi*, Abdullahi Uwaisu Muhammad, Muhammad Ibrahim, Omaji Samuel, and Prayag Tiwari, IoMT: A Medical Resource Management System using Edge Empowered Blockchain Federated Learning, accepted by IEEE Transactions on Network and Service Management, 2023.08 (SCI JCR Q1)

3 Yingchi Mao*, Lijuan Shen, Jun Wu*, Ping Ping and Jie Wu, Federated Dynamic Client Selection for Fairness Guarantee in Heterogeneous Edge Computing, Journal of Computer Science and Technology, 2023.08, (SCI, CCF-B)

4 Yong Qian, Yingchi Mao*, et, Dense video captioning based on local attention, IET Image Processing, 2023, 05, (CCF-C，SCI)

5 Zhihao Chen, Yingchi Mao*, Yong Qian, Zhenxiang Pan, and Shufang Xu, FRDet: Few-shot object detection via feature reconstruction, IET Image Processing, 2023, 07, CCF-C, SCI

6 Yingchi Mao*, Chenxin Li, Zibo Wang, Zijian Tu, and Ping Ping, Differential Privacy in Federated Dynamic Gradient Clipping Based on Gradient Norm, in Proc. of the 23rd International Conference on Algorithms, and Architecture for Parallel Processing (ICA3PP 2023), CCF-C

7 Silong Ding, Yingchi Mao*, Yong Cheng, Tianfu Pang, Lijuan Shen, and Rongzhi Qi, ECIEF: Event Causality Identification Based on Feature Fusion, accpeted by the 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2023), CCF-C

8 Tianfu Pang, Yingchi Mao*, Silong Ding, Biao Wang, and Rongzhi Qi, Script Event Prediction Based on Causal Generalization Learning, accpeted by the 35th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2023), CCF-C

9 Ling Chen, Yingchi Mao*, Hongliang Zhou, Benteng Zhang, and Jie Wu, MTS: Multivariate Time Series Anomaly Detection Based on Graph Attention Network, Int. Journal of Sensor Netowrks, 2023, 08, SCI

10  Guo Hongle, Yingchi Mao*, et, Two Phases Privacy-Preserving Scheme for Federated Learning in Edge Networks, Int. Journal of Sensor Netowrks, 2023.07, SCI

11 Jiajun Wang, Yingchi Mao*, Zibo Wang, Jun Wu, and Jie Wu, Joint Focal Loss and Dominant Gradient Correction for Gradient Conflict in Federated Learning, in Proc. of the 20^th IEEE International Conference on Mobile Ad-hoc and Smart Systems (MASS 2023), CCF-C

12 Benteng Zhang, Yingchi Mao*, Zijian Tu, Xiaoming He, Ping Ping, Jie Wu. Optimizing Privacy-Accuracy Trade-off in DP-FL via Significant Gradient Perturbation, in Proc. of the 19th International Conference on Mobility, Sensing, and Networking (MSN 2023), CCF-C

13 Yingchi Mao*, Zibo Wang, Jun Wu, Lijuan Shen, Shufang Xu, Jie Wu. Two-way Delayed Updates with Model Similarity in Communication-Efficient Federated Learning,  in Proc. of the 19th International Conference on Mobility, Sensing, and Networking (MSN 2023), CCF-C

14 Xiaoming He, Yingchi Mao*, Yinqiu Liu, Yan Hong. Green Resource Allocation with DDPG for knowledge Learning in Digital Twin-enabled Edges, in Proc. of 2023 IEEE 98th Vehicular Technology Conference (VTC 2023).

15 朱敏，毛莺池*，程永，陈程军，王龙宝，基于双重注意力机制的事件抽取方法，软件学报（T1期刊，CCF-A中文），2023,07

2022年

1 Yingchi Mao*, Jun Wu, Xiaoming He, Ping Ping, Jiajun Wang, and Jie Wu, Joint Dynamic Grouping and Gradient Coding for Time-Critical Distributed Machine Learning in Heterogeneous Edge Networks, IEEE Internet of Things Journal, 2022, 12 (JCR Q1)

2 Yingchi Mao*, Jun Wu, Xuesong Xu, Longbao Wang, Adaptive sparse ternary gradient compression for distributed DNN training in edge computing, CCF Transactions on High Performance Computing, 2022,03 (SCI) Online

3 Yingchi Mao*, Jun Wu, Yangkun Cheng, Ping Ping, Jie Wu, Local Performance Trade-Off in Heterogeneous Federated Learning with Dynamic Client Group, in Proc. of the 19^th IEEE International Conference on Mobile Ad-hoc and Smart Systems (MASS 2022), CCF-C

4 Yingchi Mao*, Jun Wu, Xiaoming He, Ping Ping, Jianxin Huang, Communication Optimization in Heterogenesou Edge Networks using Dynamic Grouping and Gradient Coding, in Proc. of the 17^th IEEE International Conference on Wireless Algorithms, Systems, and Applications (WASA 2022), CCF-C

5 Jiajun Wang, Yingchi Mao*, Xiaoming He, Tong Zhou, Jun Wu, and Jie Wu, Accelerating Federated Learning with two-phase Gradient Adjustment, in Porc. of the 27^th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2022), CCF-C

6 Jun Wu, Yingchi Mao*, Xuesong Xu, ASTC: An Adaptive Gradient Compression Scheme for Communication-Efficient Edge Computing, in Proc. of the 23^rd IEEE International Conference on High Performance Computing and Communications (HPCC-2021), CCF-C

7 Hongle Guo, Yingchi Mao*, Xiaoming He, and Hua Nie, A Blockchain-based Grouped Federated Learning Scheme Against Malicious Clients, in Proc. of the 64^th IEEE Global Communications Conference (GlobeCom 2021), CCF-C

8 Ruixiang Li, Yingchi Mao*, Tao Wu, Jun Wu, Approximate Matching Based Cache Selection Strategy in Mobile Edge Computing, in Proc. of 2021 the 9^th International Conference on Advanced Cloud and Big Data (CBD 2021), Award of Best Student Paper

9 屠子健，毛莺池*，吴明波，陈禹，基于强化学习的电力数据存储系统参数自适应调优，电力系统自动化（T1期刊），2022, Vol. 46, No. 4

10 刘意，毛莺池*，程杨堃，高建，王龙宝，基于领域一致性的异常检测序列集成方法，计算机科学（CCF-B中文），2022, Vol. 49, No. 1

11 李梦菲，毛莺池*，屠子健，王暄，徐淑芳，基于深度确定性策略梯度的服务器可靠性任务卸载策略，计算机科学（CCF-B中文），2022, Vol. 49, No. 7

12 毛莺池，唐江红*，王静，平萍，王龙宝，基于Faster R-CNN的多任务增强裂缝图像检测方法，智能系统学报（CCF-B中文），2021, Vol. 16, No. 2

13 程永，毛莺池*，万旭，王龙宝，朱敏，基于双重注意力的无触发词中文事件检测，计算机科学（CCF-B中文）