基本信息
| 姓名：杨磊 办公室：B7-413 E-mail: sely@scut.edu.cn 所在团队：软件服务工程与云计算团队 个人主页：http://www2.scut.edu.cn/sse/2018/0614/c16789a270682/page.htm |
个人简介
杨磊，华南理工大学软件学院教授、博士生导师。主要研究领域为分布式计算与网络、边缘计算与边缘智能、分布式机器学习、云计算与大数据。近年来在IEEE TMC、 IEEE TC、IEEE TPDS、IEEE TSC、IEEE TKDE等国际期刊以及IEEE INFOCOM、IEEE ICDE、ACM/IFIP Middleware等权威会议发表论文七十余篇。发表论文曾被评选为云计算知名期刊IEEE TCC的亮点论文。单篇论文Google Scholar最高引用556次，总引用3262次。主持国家自然科学基金、香港RGC-TRS、广东省自然科学基金、广州市科技计划、CCF-腾讯犀牛鸟以及其他企业横向课题等十余项。授权发明专利11件。荣获2018年高等学校科学研究优秀成果奖（科学技术）自然科学二等奖、2020年国际电子电气工程师学会云计算专委会（IEEE TCCLD）研究创新奖。入选斯坦福大学全球前2%顶尖科学家榜单。担任广东省计算机学会移动与边缘计算专委主任；中国计算机学会互联网专委、分布式计算与系统专委执行委员。2014年获香港理工大学电子计算学系博士学位。2014年11月-2016年2月任香港理工大学博士后研究员。2012年-2013年在德国达姆斯塔特工业大学交流访问。
学历
2010.7 - 2014.5， 香港理工大学电子计算学系，博士
2007.9 - 2010.6， 中国科学院计算技术研究所，硕士
2003.9 - 2007.7， 武汉大学电子信息学院，学士
教学经历
主讲本科生课程：《软件体系结构》、《数据库系统》、《数据库实训》
主讲研究生课程：《云计算技术》
工作经历
2022.9 - 至今，华南理工大学，教授
2016.3 - 2022.8，华南理工大学，副教授
2014.11 - 2016.2，香港理工大学，博士后
社会兼职
CCF互联网专委、CCF分布式计算与系统专委执行委员
研究方向
分布式计算与网络、边缘计算与边缘智能、分布式机器学习、云计算与大数据
获奖情况
2018高等学校科学研究优秀成果奖（科学技术）自然科学二等奖（3/4）
2020 IEEE TCCLD Research Innovation Award (2/3)
科研项目
1.国家自然科学基金面上项目，“协作式边缘计算中网络感知的任务调度技术研究”，2020/01 - 2023/12
2.香港RGC-TRS项目，“高性能协同边缘计算框架、方法及其在智慧城市中的应用”，2024/01 - 2028/12 （Co-PI）
3.广东省自然科学基金面上项目，“边缘计算中基于多层次结构的分布式模型训练方法研究”，2022/01-2024/12
4.广州市基础与应用基础研究项目，“面向边缘智能的分布式模型学习方法研究”，2022/04-2024/03
5.教育部产学合作协同育人项目，“《云计算技术》课程创新实践”，2021/01-2022/01
6. 广东省自然科学基金自由申请项目，“面向差异化应用模型的边缘云性能优化技术研究”，2019/10 – 2022/09
7.中央高校科研业务经费，“移动边缘计算性能建模与优化”，2018/09-2020/08
8.国家自然科学基金青年项目, “移动云计算中数据流应用的动态计算切分技术研究”，2016/01–2018/21
9.华为高校技术合作项目，“5G垂直行业超高可靠性研究”，2019/01-2020/01
10. CCF-腾讯犀牛鸟创意基金，“基于用户行为分析的移动应用隐私保护技术”，2016/09- 2017/08
发表文章（*通讯作者）
1.Jiajun Yao, Lei Yang(*), Hao Liu, Hui Xiong. Joint Dependency and Conflicting Task Allocation in Collaboration-aware Spatial Crowdsourcing. 41st IEEE International Conference on Data Engineering (ICDE). May 19-23, 2025, Hong Kong, China.
2.Hao Cheng , Lei Yang(*), Qingfeng Zhang , Weiping Zhu. Reliable Routing and Scheduling in Time Sensitive Networks based on Reinforcement Learning. IEEE Transactions on Network Science and Engineering, DOI: 10.1109/TNSE.2025.3546100
3.Jingke Tu, Lei Yang(*), Jiannong Cao. Distributed Machine Learning in Edge Computing: Challenges, Solutions and Future Directions. ACM Computing Surveys, 57(5): 132:1-132:37, January, 2025
4.Yuesheng Tan, Lei Yang(*), Wenhao Li, Yuda Wu. RoleML: a Role-Oriented Programming Model for Customizable Distributed Machine Learning on Edges. 25th ACM/IFIP International Middleware Conference (Middleware 2024). December 2024, Hong Kong, China
5.Zhongyun Zhang, Lei Yang (*), Jiajun Yao, Chao Ma, Jianguo Wang. Joint Optimization of Pricing, Dispatching and Repositioning in Ride-hailing with Multiple Models Interplayed Reinforcement Learning. IEEE Transactions on Knowledge and Data Engineering. 36(12):8593-8606. December 2024 (CCF-A)
6.Yanyan Lu, Lei Yang(*), Haorui Chen, Jiannong Cao, Wanyu Lin, Saiqin Long. Federated Class-Incremental Learning with Dynamic Feature Extractor Fusion. IEEE Transactions on Mobile Computin. DOI: 10.1109/TMC.2024.3419096, June 2024 (CCF-A)
7.Hongcai Lin, Lei Yang(*), Hao Guo, Jiannong Cao. Decentralized Task Offloading in Edge Computing: An Offline-to-Online Reinforcement Learning Approach. IEEE Transactions on Computers. 73(6): 1603 - 1615, June, 2024 (CCF-A)
8.Lei Yang, Yingqi Gan, Jinru Chen, Jiannong Cao. AutoSF: Adaptive Distributed Model Training in Dynamic Edge Computing. IEEE Transactions on Mobile Computing. 23(6):6549-6562, June 2024 (CCF-A).
9.Haorui Chen, Lei Yang(*), Xinglin Zhang, Jiaxing Shen and Jiannong Cao. Distributed Semi-Supervised Learning with Consensus Consistency on Edge Devices. IEEE Transactions on Parallel and Distributed Systems. 35(2): 310-323, February, 2024 (CCF-A)
10.Jingke Tu, Jiaming Huang, Lei Yang(*), Wanyu Lin. Personalized Federated Learning with Layer-Wise Feature Transformation via Meta-Learning. ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 4, Article No.: 99, pp 1–21, February 2024
11.Jiajun Yao, Lei Yang(*), Zhenyu Wang, Xiaohua Xu. Non-rejection aware Online Task Assignment in Spatial Crowdsourcing. IEEE Transactions on Services Computing. 16(6): 4540-4553, December 2023 (CCF-A)
12.Jiajun Yao, Lei Yang(*), Xiaohua Xu. Online Dependent Task Assignment in Preference Aware Spatial Crowdsourcing. IEEE Transactions on Services Computing. 6(14): 2827-2840, July 2023（CCF-A）
13.Lei Yang, Junzhong Jia, Hongcai Lin, Jiannong Cao. Reliable Dynamic Service Chain Scheduling in 5G Networks. IEEE Transactions on Mobile Computing. 22(8):4898-4911, August, 2023 （CCF-A）
14.Lei Yang, Yingqi Gan, Jiannong Cao, Zhenyu Wang. Optimizing Aggregation Frequency for Hierarchical Model Training in Heterogeneous Edge Computing. IEEE Transactions on Mobile Computing. 22(7): 4183-4194, July, 2023 (CCF-A)
15.Ziqi He, Lei Yang(*), Wanyu Lin, Weigang Wu. Improving Accuracy and Convergence in Group-based Federated Learning on Non-IID Data. IEEE Transactions on Network Science and Engineering. 10(3): 1389-1404, May 2023
16.Lei Yang, Jiaming Huang, Wanyu Lin, Jiannong Cao. Personalized Federated Learning on Non-IID Data via Group-Based Meta-Learning. ACM Transactions on Knowledge Discovery from Data. 17(4):1-20, March 2023.
17.Lei Yang, Fulin Wen, Jiannong Cao, Zhenyu Wang. EdgeTB: a Hybrid Testbed for Distributed Machine Learning at the Edge with High Fidelity. IEEE Transactions on Parallel and Distributed Systems. 33(10):2540-2553, October, 2022 (CCF-A)
18.Shaoshuai Ding, Lei Yang (*), Jiannong Cao, Wei Cai, Mingkui Tan, Zhenyu Wang. Partitioning Stateful Data Stream Applications in Dynamic Edge Cloud Environments. IEEE Transactions on Services Computing. 15(4): 2368 – 2381, July-Aug. 2022 (CCF-A)
19.Lei Yang, Xi Yu, Jiannong Cao, Wengen Li, Yuqi Wang, Michal Szczecinski. A Novel Demand Dispatching Model for Autonomous On-Demand Services. IEEE Transactions on Services Computing. 5(1): 322-333 , Jan 2022 (CCF-A)
20.Lei Yang, Jiannong Cao, Zhenyu Wang, Weigang Wu. Network aware Mobile Edge Computation Partitioning in Multi-user Environments. IEEE Transactions on Services Computing. Vol.14, No. 5, Sept.- Oct., 2021, pp. 1478 – 1491. (CCF-A)
21.Lei Yang, Bo Liu, Jiannong Cao, Yuvraj Sahni, Zhenyu Wang. Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Cloud. IEEE Transactions on Services Computing. Vol.14, No. 5, Sept.- Oct., 2021, pp. 1439 – 1452. (CCF-A)
22.Lei Yang, Xi Yu, Jiannong Cao, Xuxun Liu, Pan Zhou. Exploring Deep Reinforcement Learning for Demand Dispatching in Autonomous On-Demand Services. ACM Transactions on Knowledge Discovery from Data. Vol.15, Issue 3, Article No.:44, pp 1-23, April 2021
23.Junzhong Jia, Lei Yang*, Jiannong Cao. Reliability-aware Dynamic Service Chain Scheduling in 5G Networks based on Reinforcement Learning. IEEE International Conference on Computer Communications (INFOCOM 2021). (CCF-A)
24.Lei Yang, Yanyan Lu, Jiannong Cao*, Jiaming Huang, Mingjin Zhang. E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI. IEEE Internet of Things Journal. 8(14): 11290-11304, July 2021
25.Lei Yang, Lingling Zhang, Zongjian He, Jiannong Cao, Weigang Wu. Efficient Hybrid Data Dissemination in Edge Assisted Automated Driving. IEEE Internet of Things Journal.7(1):148 – 159, 2020
26.Jin Cao, Lei Yang(*), Jiannong Cao. Revisiting Computation Partitioning in Future 5G based Edge Computing Environments. IEEE Internet of Things Journal, 6(2): 2427 – 2438, 2019
27.Lei Yang, Jiannong Cao, Guanqing Liang, Xu Han. Cost Aware Service Placement and Load Dispatching in Mobile Cloud Systems. IEEE Transactions on Computers. 65(5): 1440-1452, 2016. (CCF-A)
28.Lei Yang, Jiannong Cao, Shaojie Tang, Di Han, Neeraj Suri. Run Time Application Repartitioning in Dynamic Mobile Cloud Environments. IEEE Transactions on Cloud Computing. 4(3): 336-348, 2016.
29.Lei Yang, Jianong Cao, Weiping Zhu, Shaojie Tang. Accurate and Efficient Object Tracking based on Passive RFID. IEEE Transactions on Mobile Computing. 14(11): 2188-2200, 2015. (CCF-A)
30.Lei Yang, Jiannong Cao, Hui Cheng, and Yusheng Ji. Multi-user Computation Partitioning for Latency Sensitive Mobile Cloud Applications. IEEE Transactions on Computers, 64(8): 2253-2266, 2015 (CCF-A)
31.Lei Yang, Jiannong Cao, Yin Yuan, Tao Li, Andy Han, Alvan Chan. A Framework for Partitioning and Execution of Data Stream Application in Mobile Cloud Computing. ACM SigMetrics Performance Evaluation Review, vol 40, no.4, pp.23-32, 2013
授权专利
[1] 李宏韬，杨磊. 一种应用于TSN网络的冗余流调度方法，2021.12，中国，ZL202011306704.3
[2] 梁俊鹏，杨磊. 一种数据中心里分布式机器学习数据重排的传输优化方法，2021.10，中国，ZL202010611841.1
[3] 张玲玲，杨磊. 一种基于边缘计算的车联网混合数据分发方法，2021.09，中国，ZL201910349740.9
[4] 杨磊. 配置数据处理方法及装置，2021.03，中国，ZL2017103996427
[5] 刘波，杨磊. 一种针对边缘计算环境的任务协同在线调度方法，2023.06，中国，ZL2019104055720
[6] 丁绍帅，杨磊. 一种针对有状态数据流应用的计算卸载方法，2023.01, 中国，ZL2019105360203
[7] 余玺，杨磊. 一种基于按需服务的多对多需求分配方法，2023.07，中国，ZL2019102808133
[8] 贾俊中，杨磊. 一种服务链中动态资源分配和任务调度方法，2024.03，中国，ZL2020114334751
[9] 杨磊，甘颖棋. 一种针对层次化模型训练框架的聚合频率控制方法，2024.03，中国，ZL202111535624X
[10] 杨磊，何紫琦. 一种针对分布式边缘学习中的模型聚合的分组优化方法. 2024.10, 中国，ZL2021116031777
[11] 杨磊，黄家明. 一种针对数据异构性的个性化联邦元学习方法，2024.06，中国，ZL2021115356269
