吴文泰，男，博士（Ph.D. in Computer Science），副教授。主要研究兴趣包括分布式系统、边缘智能、可持续计算和协同机器学习。在相关学术领域发表期刊和会议论文20余篇，获IEEE Computer Society 2021年最佳论文奖第二名（best paper award runner-up），2020年广东省科技进步二等奖；参编《Data Center Handbook: Plan, Design, Build, and Operations of a Smart Data Center》（2nd Edition）等英文专著2本，担任IEEE TPDS、TMC、TBD、TSUSC等高影响力期刊和NeurIPS、ICML等顶级会议的审稿人，《计算机科学》期刊“联邦学习技术及前沿应用”专栏特邀编审。被列入2023年度分布式计算领域复合引用指标全球前2%科学家（top 2% scientist, single year，2023，by Stanford）。

个人主页：https://wingter562.github.io/wentai_homepage/

2011-2015，华南理工大学，工学学士

2015-2018，华南理工大学，工学硕士

2018-2022，英国华威大学（University of Warwick），博士（CSC国家公派博士留学生）

2022-2023，鹏城实验室，新型网络研究部产业互联网所，助理研究员

2024至今，暨南大学，信息科学技术学院计算机科学系，副教授

分布式系统、协同计算、可持续计算、协同机器学习

部分成果列表：

[] Lin, W., Wang, S., Wu, W.*, Li, D., & Zomaya, A. (2023) HybridAD: A Hybrid Model-driven Anomaly Detection Approach for Multivariate Time Series. IEEE Transactions on Emerging Topics in Computational Intelligence. Vol.8, no.1, pp.866-878. DOI: 10.1109/TETCI.2023.3290027. [JCR-Q2, IF 5.3]

[] Wu, W., He, L.*, Lin, W.*, & Maple, C. (2023) FedProf: Selective Federated Learning based on Distributional Representation Profiling. IEEE Transactions on Parallel and Distributed Systems (TPDS). Vol. 34, no. 6, pp. 1942-1953. DOI: 10.1109/TPDS.2023.3265588. [CCF-A, JCR-Q1, IF 5.3]

[] Lin, W., Xiong, C.*, Wu, W.*, Shi, F., Li, K., & Xu, M. (2022). Performance Interference of Virtual Machines: A Survey. ACM Computing Surveys. Vol. 55, no. 12, pp. 1-37 [JCR-Q1, IF 16.6]

[] Wu, W., He, L.*, Lin, W., & Mao, R. (Jul, 2021) Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems. IEEE Transactions on Parallel and Distributed Systems (TPDS). vol. 32, no.7, pp. 1539-1551. [CCF-A, JCR-Q1, IF 5.3]

[] Wu, W., He, L.*, Lin, W., Mao, R., & Jarvis, S. (Jun, 2021). SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead. IEEE Transactions on Computers (TC). vol. 70, no.5, pp. 655-668. [CCF-A, JCR-Q2, IF 3.7, IEEE Computer Society 2021 Best Paper Award Runner-up (from IEEE TC)]

[] Wu, W., He, L.*, Lin, W. et al. (Sep, 2022). Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality. IEEE Transactions on Knowledge and Data Engineering (TKDE). Vol. 34, no. 9, pp. 4147-4160. [CCF-A, JCR-Q1, IF 8.9]

[] Wu, W., Lin, W.*, He, L., Wu, G., & Hsu, C. (Apr, 2021). A Power Consumption Model for Cloud Servers Based on Elman Neural Network. IEEE Transactions on Cloud Computing (TCC). Vol. 9, no. 4, pp. 1268-1277. [JCR-Q1, IF 6.5]

[] Lin, W., Wu, W.*, & He, L.(Mar, 2022). An On-line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers. IEEE Transactions on Services Computing (TSC). Vol. 15, no. 2, pp. 766-777. [CCF-A, JCR-Q1, IF 8.1]

[] Wu, W., Lin, W.*, Hsu C., & He, L. (Sep, 2018). Energy-Efficient Hadoop for Big Data Analytics and Computing: A Systematic Review and Research Insights. Future Generation Computer Systems (FGCS). vol. 86, pp. 1351-1367. DOI: 10.1016/j.future.2017.11.010. [JCR-Q1, IF 7.5]

[] Lin, W.*, Wu, W.*, Wang, H., Wang, J. & Hsu, C. (Sep, 2018). Experimental and Quantitative Analysis of Server Power Model for Cloud Data Centers. Future Generation Computer Systems (FGCS). Vol. 86, no. 5, pp. 940-950. DOI: 10.1016/j.future.2016.11.034. [JCR-Q1, IF 7.5]

[] Wu, W., Lin, W.*, & Peng, Z. (Oct, 2017). An intelligent power consumption model for virtual machines under CPU-intensive workload in cloud environment. Soft Computing, vol. 21, no. 19, pp. 5755–5764. DOI: 10.1007/s00500-016-2154-6. [JCR-Q2, IF 4.1]

[] Lin, W., Wu, W.*, & Wang, J. (May, 2016). A heuristic task scheduling algorithm for heterogeneous virtual clusters. Scientific Programming, vol. 2016, pp. 1-10. DOI:10.1155/2016/7040276. [JCR Q3, IF 1.672]

[] 林伟伟, 吴文泰*. 面向云计算环境的能耗测量和管理方法. 软件学报, 2016, 27(4): 1026 -1041. DOI: 10.13328/j.cnki.jos.005022. [EI, IF 3.644]

[] Wu, Y. & Wu, W*. (2015). Modeling Topic Popularity Distribution and Evolution in an Online Discussion Forum. Journal of Computational Information Systems (ISSN: 1553-9105), vol. 11, no. 18, pp. 6797-6810. [EI]*

[] Shi, F., Hu, C., Lin, W.*, Fan, L., Huang, T., & Wu, W. (2022). VFedCS: Optimizing Client Selection for Volatile Federated Learning. IEEE Internet of Things Journal. 2022. DOI: 10.1109/JIOT.2022.3172113. [JCR-Q1, IF 10.238]

[] Huang, T., Lin, W.*, Wu, W, He, L., Li, K., & Zomaya, A.Y. (2021) An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee. IEEE Transactions on Parallel and Distributed Systems (TPDS). Vol. 32, pp. 1552-1564. DOI: 10.1109/TPDS.2020.3040887. [CCF-A, JCR-Q1, IF 3.757]

· Wu, W., Lin, W., & Li, K. (2023). Energy Efficiency of Servers in Data Centers. Encyclopedia of Sustainable Technologies, 2nd Edition. Amsterdam, Netherlands: Elsevier. [ISBN: 9780124095489] <https://www.sciencedirect.com/science/article/abs/pii/B9780323903868000541>

· Lin, W., Wu, W., & Li, K. (May 2021). Chapter 19: Energy Saving Technologies of Servers in Data Centers, in Geng, H. (Ed.), Data Center Handbook: Plan, Design, Build, and Operations of a Smart Data Center, 2nd Edition. Hoboken, NJ: John Wiley & Sons. [ISBN: 9781119597506]

参与课题：

国家自然科学基金项目面上项目（61872150，移动边缘计算环境下高效可信的协同计算关键技术研究，2019/01-2022/12，61.0万，8/10）

国家自然科学基金项目面上项目（61772205，面向云计算的虚拟机能耗模型及其应用方法研究，2018/01-2021/12，63.0万，7/9）

广东省科技计划项目(应用型专项，2017B010126002，基于大数据的保险业潜客识别关键技术研发与应用推广，2017/01-2019/12，800.0万，14/23）

广东省科技计划项目（产学研项目，2016B090918021，基于大数据技术的BI系统研发，2016/01-2018/12，100.0万，11/17）

广东省科技计划项目（工业高新技术领域，2016A010101007，云计算能耗建模与优化技术研究，2016/01-2017/12，30.0万，4/10）

广州市科技计划项目（201604010040，云宏云计算管理平台的智能管理关键技术研发，2015/04-2017/03，200.0万，14/18）

林伟伟,王浩宇,吴文泰.一种基于部件能耗模型的云服务器能耗测算方法及系统[P].中国:ZL201710924039.6,2021年1月19日

2019/10-2020/03, CS140 Computer Security, Associate Tutor

2024年至今，08060231 《计算机组成原理》，主讲教师