科研团队
范晓亮
高级工程师、硕士生导师
fanxiaoliang@xmu.edu.cn

范晓亮,法国巴黎六大计算机科学博士(2012),兰州大学计算机科学与技术学士(2004)。厦门大学信息学院高级工程师,数字福建城市交通大数据研究所(厦门大学)常务副所长,福建省智慧城市感知与计算重点实验室党支部书记,福建省B类高层次人才,厦门市高层次留学人员创新创业服务团成员。研究兴趣:可信人工智能、联邦学习、行业大模型应用。主持3项国家自然科学基金和百度、腾讯、厦门地铁等产学研项目,牵头国内首个高等教育隐私计算标准。TKDE、TSC、TMC、NeurIPS、AAAI、CVPR、KDD、IJCAI、WWW等人工智能领域顶级期刊或会议发表论文80余篇(单篇最高引用1600+次,含ESI高引论文1篇、CCF A类论文15篇)。获中国计算机学会CCF服务计算青年才俊奖、福建省科技进步一等奖。美国计算机学会ACM高级会员,国际电气电子工程师学会IEEE高级会员,IEEE教育数据挖掘工作组副主席,中国计算机学会CCF杰出会员、CCF厦门分部执委、CCF服务计算专委会执委、CCF普适计算专委会执委。2024年“数据要素×”大赛福建分赛评审专家、厦门市信息化项目评审专家,以及厦门市公安局、大数据管理局、交通运输局、体育局等咨询评审专家。

完整论文+开源代码:https://xiaoliangfan.github.io/


推免生或考研招生

  本实验室硕士生及博士生的培养方向为科技公司的算法岗,以及大学和研究机构的研究岗。欢迎志向远大、身心健康、乐于合作、学业优秀的同学们加入实验室!请感兴趣的同学邮件联系我(fanxiaoliang@xmu.edu.cn)。


主持科研项目(部分)

  • 面向时空图的高效安全联邦学习关键问题研究,国家自然科学基金面上基金项目62272403),范晓亮(主持),20231-202612月,54万元

  • 大规模人群出行的不确定性分析与城市级别人流预测研究,国家自然科学基金面上基金项目61872306),范晓亮(主持)20191-202212月,64万元,已结题

  • 情境感知云计算工作流的动态服务选择研究,国家自然科学基金青年科学基金项目61300232),范晓亮(主持),20141-201612月,23万,已结题

  • 多时空尺度用电需求分析推演方法研究,国网福建委托项目(国网登高计划课题),范晓亮(主持),2025年3月-2026年12月,64.52万元

  • 面向用电需求多场景预测推演泛化提升的MOE大模型研究,国网福建委托项目,范晓亮(主持),2025年3月-2026年12月,92.87万元

  • 基于飞桨平台的联邦学习与大模型精调算法研究,CCF-百度松果基金CCF-BAIDU OF2022016),范晓亮(主持),20229-20238月,10万元,已结题

  • 城轨云、大数据应用关键指标专题研究项目,企事业委托项目(厦门轨道交通集团有限公司),范晓亮(主持),20212-20222月,39.34455万元,已结题

  • 面向时空相关性挖掘的情境感知Web服务推荐算法研究,中国博士后科学基金面上项目一等资助(2015M580564),范晓亮(主持),20156月-20175月,8万,已结题


学生就业去向(部分)

  • 学术界:鹏城实验室(王子徽,2024届博士)、复旦大学(洪庚,保送复旦大学博士,2018届学士)

  • 工业界:理想汽车(彭朝鹏,2025届硕士,美团实习)、蚂蚁金服(金海炳,2025届硕士,蚂蚁金服实习)、蚂蚁金服(吴尚斌,2022届硕士,蚂蚁金服实习)、Bigo(闫旭,2022届硕士,美团实习)、上海宏景智驾(陈亮,2022届硕士)、建信金科(高桂春,2021届硕士)、阿里(朱耀,保送北大交叉研究院,2019届学士)

  • 其他:福建省公安厅选调(郑传潘,2023届博士)、合肥事业单位(杨志成,2024届硕士)、南方电网(肖璐菁,2021届硕士)


学术获奖(部分)

  • CCF服务计算青年才俊奖,范晓亮(序1),2022年8月,中国计算机学会CCF服务计算专委会

  • 第九届厦门大学大学生创新年会安踏创新专项奖励基金优秀创新教育指导教师,范晓亮,2025年3月,厦门大学

  • 基于大模型技术的高效安全数据资产价值评估系统,中国国际大学生创新大赛(2024)产业赛道国赛银奖,指导教师(范晓亮,序1)

  • 联邦学习大模型精调策略与产业实践,中国国际大学生创新大赛(2023)产业赛道铜奖,指导教师(范晓亮,序1)

  • 重型货车运输安全监测与保险风控AI应用,2024年数据要素×大赛福建分赛交通运输赛道金奖(第一名),指导教师(范晓亮),福建省大数据管理局

  • CSC-IBM中国优秀教师奖教金,范晓亮(序1),201410月,国家留学基金管理委员会

  • 法国埃菲尔卓越博士奖学金,范晓亮(序1),20103月,法国外交部(编号:690544G


教育经历

  • 2012年,法国巴黎第六大学(现更名:索邦大学,全法排名第一),计算机科学,博士

  • 2012年,兰州大学,计算数学,工学博士

  • 2007年,兰州大学,计算机应用,硕士研究生(提前攻博)

  • 2004年,兰州大学,计算机科学与技术,学士


产业化应用案例

第一,GMAN图多注意力机制的城市交通流量预测平台(https://github.com/zhengchuanpan/GMAN

  • GMAN: A Graph Multi-Attention Network for Traffic Prediction已发表在AAAI-20 Google引用1600+Github350+ starsAAAI-20最具影响力论文排名第三),城市级路网流量1小时以上预测精度全球领先

  • 应用:20203为厦门市疫情回流风险预估提供决策支撑。厦门市领导批示,学习强国、光明日报报道

第二,FLGo联邦学习轻量级开源平台(Github: 550+ starshttps://flgo-xmu.github.io

  • 算法:集成经典和最新的联邦学习算法和benchmarks50+),并与百度飞桨、蚂蚁隐语等国产化平台适配

  • 应用:自研两个联邦学习算法FedFVIJCAI-21)和FedGSAAAI-23)部署在百度的商业化联邦学习平台

第三,厦门市交通大数据分析应用平台(部署在厦门市大数据安全开放平台https://data.xm.gov.cn

  • 算法:平台汇聚厦门市全量交通传感数据100TB,研发20+交通AI算法和交管大模型助手支撑厦门市公安局交警支队和厦门市交通局各类系统调用5+/,服务1亿+用户出行,为相关企业新增产值2亿元

  • 应用:服务2017年金砖国家领导人厦门会晤等重大活动交通安保任务,获2018年福建省科技进步一等奖



研究方向 Research 发表的数据集 Datasets 部分论文列表 Selected Publications
2025
Zheng Wang, Zihui Wang, Zheng Wang, Xiaoliang Fan, Cheng Wang*
Federated Learning with Domain Shift Eraser
CVPR-25 (CCF A类会议)
@article{wang2025federated, title={Federated Learning with Domain Shift Eraser}, author={Wang, Zheng and Wang, Zihui and Fan, Xiaoliang and Wang, Cheng}, journal={arXiv preprint arXiv:2503.13063}, year={2025} }
Zheng Wang, Wanwan Wang, Yimin Huang, Zhaopeng Peng, Ziqi Yang, Cheng Wang, Xiaoliang Fan*
P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Network
WWW-25 (CCF A类会议)
@article{wang2024p4gcn, title={P4GCN: Vertical Federated Social Recommendation with Privacy-Preserving Two-Party Graph Convolution Networks}, author={Wang, Zheng and Wang, Wanwan and Huang, Yimin and Peng, Zhaopeng and Yang, Ziqi and Wang, Cheng and Fan, Xiaoliang}, journal={arXiv preprint arXiv:2410.13905}, year={2024} }
2024
Zihui Wang, Zhaopeng Peng, Xiaoliang Fan*, Zheng Wang, Shangbin Wu, Rongshan Yu, Peizhen Yang, Chuanpan Zheng, Cheng Wang
FedAVE: Adaptive Data Value Evaluation Framework for Collaborative Fairness in Federated Learning
Neurocomputing
@article{wang2024fedave, title={FedAVE: Adaptive data value evaluation framework for collaborative fairness in federated learning}, author={Wang, Zihui and Peng, Zhaopeng and Fan, Xiaoliang and Wang, Zheng and Wu, Shangbin and Yu, Rongshan and Yang, Peizhen and Zheng, Chuanpan and Wang, Cheng}, journal={Neurocomputing}, volume={574}, pages={127227}, year={2024}, publisher={Elsevier} }
Ziqi Yang, Zhaopeng Peng, Zihui Wang, Jianzhong Qi, Chaochao Chen, Weike Pan, Chenglu Wen, Cheng Wang, Xiaoliang Fan*
Federated Graph Learning for Cross-Domain Recommendation
NeurIPS-24 (CCF A类会议)
@article{yang2024federated, title={Federated Graph Learning for Cross-Domain Recommendation}, author={Yang, Ziqi and Peng, Zhaopeng and Wang, Zihui and Qi, Jianzhong and Chen, Chaochao and Pan, Weike and Wen, Chenglu and Wang, Cheng and Fan, Xiaoliang}, journal={arXiv preprint arXiv:2410.08249}, year={2024} }
Zhaopeng Peng, Xiaoliang Fan*, Cheng Wang, et al.
FedPFT: Federated Proxy Fine-Tuning of Foundation Models
IJCAI-24 (CCF A类会议)
@article{peng2024fedpft, title={FedPFT: Federated Proxy Fine-Tuning of Foundation Models}, author={Peng, Zhaopeng and Fan, Xiaoliang and Chen, Yufan and Wang, Zheng and Pan, Shirui and Wen, Chenglu and Zhang, Ruisheng and Wang, Cheng}, journal={arXiv preprint arXiv:2404.11536}, year={2024} }
Zihui Wang, Cheng Wang, Xiaoliang Fan*, et al.
FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning
KDD-24 (CCF A类会议)
@article{wang2024fedsac, title={FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning}, author={Wang, Zihui and Wang, Zheng and Lyu, Lingjuan and Peng, Zhaopeng and Yang, Zhicheng and Wen, Chenglu and Yu, Rongshan and Wang, Cheng and Fan, Xiaoliang}, journal={arXiv preprint arXiv:2405.18291}, year={2024} }
Zihui Wang, Peizhen Yang, Xiaoliang Fan*, Cheng Wang, et al.,
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs
Neural Networks
@article{wang2024contig, title={Contig: Continuous representation learning on temporal interaction graphs}, author={Wang, Zihui and Yang, Peizhen and Fan, Xiaoliang and Yan, Xu and Wu, Zonghan and Pan, Shirui and Chen, Longbiao and Zang, Yu and Wang, Cheng and Yu, Rongshan}, journal={Neural Networks}, volume={172}, pages={106151}, year={2024}, publisher={Elsevier} }
Chuanpan Zheng, Xiaoliang Fan*, Cheng Wang, et al.
Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
IEEE Transactions on Knowledge and Data Engineering (CCF A类期刊, ESI高被引论文)
@article{zheng2023spatio, title={Spatio-temporal joint graph convolutional networks for traffic forecasting}, author={Zheng, Chuanpan and Fan, Xiaoliang and Pan, Shirui and Jin, Haibing and Peng, Zhaopeng and Wu, Zonghan and Wang, Cheng and Philip, S Yu}, journal={IEEE Transactions on Knowledge and Data Engineering}, year={2023}, publisher={IEEE} }
2023
Chuanpan Zheng, Xiaoliang Fan, Cheng Wang*, Jianzhong Qi, et al.
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging
WWW-23 (CCF A类会议)
@inproceedings{zheng2023increase, title={Increase: Inductive graph representation learning for spatio-temporal kriging}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wang, Cheng and Qi, Jianzhong and Chen, Chaochao and Chen, Longbiao}, booktitle={Proceedings of the ACM Web Conference 2023}, pages={673--683}, year={2023} }
Zheng Wang, Xiaoliang Fan*, Jianzhong Qi, Haibing Jin, Peizhen Yang, Siqi Chen, Cheng Wang
FedGS: Federated Graph-based Sampling with Arbitrary Client Availability
AAAI-23 (CCF A类会议)
@inproceedings{wang2023fedgs, title={Fedgs: Federated graph-based sampling with arbitrary client availability}, author={Wang, Zheng and Fan, Xiaoliang and Qi, Jianzhong and Jin, Haibing and Yang, Peizhen and Shen, Siqi and Wang, Cheng}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={37}, number={8}, pages={10271--10278}, year={2023} }
2022
Chuanpan Zheng#, Cheng Wang*, Xiaoliang Fan, et al.
STPC-Net: Learn Massive Geo-sensory Data as Spatio-Temporal Point Clouds
IEEE Transactions on Intelligent Transportation Systems
@article{zheng2021stpc, title={STPC-Net: Learn massive geo-sensory data as spatio-temporal point clouds}, author={Zheng, Chuanpan and Wang, Cheng and Fan, Xiaoliang and Qi, Jianzhong and Yan, Xu}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={23}, number={8}, pages={11314--11324}, year={2022}, publisher={IEEE} }
Shangbin Wu, Xiaoliang Fan*, Cheng Wang, et al.
Multi-Graph Fusion Networks for Urban Region Embedding
IJCAI-22 (CCF A类会议)
@article{wu2022multi, title={Multi-graph fusion networks for urban region embedding}, author={Wu, Shangbin and Yan, Xu and Fan, Xiaoliang and Pan, Shirui and Zhu, Shichao and Zheng, Chuanpan and Cheng, Ming and Wang, Cheng}, journal={arXiv preprint arXiv:2201.09760}, year={2022} }
2021
Xiaoliang Fan, Yakun Hu, Zibin Zheng*, et al.
CASR-TSE: Context-Aware Web Services Recommendation for Modeling Weighted Temporal-Spatial Effectiveness
IEEE Transactions on Services Computing (CCF A类期刊)
@article{fan2017casr, title={CASR-TSE: Context-aware web services recommendation for modeling weighted temporal-spatial effectiveness}, author={Fan, Xiaoliang and Hu, Yakun and Zheng, Zibin and Wang, Yujie and Br{\'e}zillon, Patrick and Chen, Wenbo}, journal={IEEE Transactions on Services Computing}, volume={14}, number={1}, pages={58--70}, year={2021}, publisher={IEEE} }
Zheng Wang, Xiaoliang Fan*, Jianzhong Qi, Cheng Wang, et al.
Federated Learning with Fair Averaging
IJCAI-21 (CCF A类会议)
@article{wang2021federated, title={Federated learning with fair averaging}, author={Wang, Zheng and Fan, Xiaoliang and Qi, Jianzhong and Wen, Chenglu and Wang, Cheng and Yu, Rongshan}, journal={arXiv preprint arXiv:2104.14937}, year={2021} }
2020
Chuanpan Zheng, Xiaoliang Fan*, Cheng Wang, et al.
GMAN: A Graph Multi-Attention Network for Traffic Prediction
AAAI-20 (CCF A类会议)
@inproceedings{zheng2020gman, title={Gman: A graph multi-attention network for traffic prediction}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wang, Cheng and Qi, Jianzhong}, booktitle={Proceedings of the AAAI conference on artificial intelligence}, volume={34}, number={01}, pages={1234--1241}, year={2020} }
2019
Chuanpan Zheng; Xiaoliang Fan*, Cheng Wang et al.
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
IEEE Transactions on Intelligent Transportation Systems
@article{zheng2019deepstd, title={DeepSTD: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction}, author={Zheng, Chuanpan and Fan, Xiaoliang and Wen, Chenglu and Chen, Longbiao and Wang, Cheng and Li, Jonathan}, journal={IEEE Transactions on Intelligent Transportation Systems}, volume={21}, number={9}, pages={3744--3755}, year={2019}, publisher={IEEE} }