Research


I have particular interest in Distributed Systems, Collaborative Machine Learning and Sustainable Computing. I am also involved in research on general machine learning techniques and AI-driven applications in various emerging forms of Distributed Systems.

AREAS OF INTEREST

  • Distributed Systems with Sustainability
  • Collaborative Machine Learning & Deep Learning
  • Ubiquitous Computing & Artificial Intelligence
  • Big Data & AI Applications

AWARDS & HONOUR

  • Top 2% scientists in Distributed Computing subfield per composite citation indicators, by Stanford, single year, 2023 & 2024（分布式计算领域复合引用指标2023、2024年度全球前2%科学家，中国籍 Top 20）
  • IEEE Computer Society Best Paper Award Runner-up (2021)
  • 广东省科技进步二等奖（“云计算调度优化技术”，2020）
  • 第一届中国大学生大数据研究论文与创意软件竞赛二等奖（2016）

RECENT PUBLICATIONS

  • Peng, P., Wu, W.*, Lin, W.*, Zhang, F., Liu, Y. and Li, K. (2024) Reliable Task Offloading in Sustainable Edge Computing With Imperfect Channel State Information. IEEE Transactions on Network and Service Management, doi: 10.1109/TNSM.2024.3456568.
  • Peng, P., Lin, W., Wu, W.*, Zhang, H., Peng, S., Wu, Q., & Li, K. (2024) A survey on Computation Offloading in Edge Systems: from the Perspective of Deep Reinforcement Learning Approaches. Computer Science Review. Vol. 53, article num. 100656, pp. 1-26.
  • Lin, S., Lin, W. Wu, W., Chen H., Yang. J. (2024). SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameters. The 41th International Conference on Machine Learning (ICML'24), Vienna, Austria. [oral]
  • Lin, J., Lin, W*, Wu, W., Lin, W., Li, K. (2024) Energy-aware virtual machine placement based on a holistic thermal model for cloud data centers. Future Generation Computer Systems (FGCS). Vol. 161, pp.302-314.
  • Li, Z., Zeng, X., Xiao, Y., Li, C., Wu, W., Liu, H. (2024) Pattern-sensitive Local Differential Privacy for Finite-Range Time-series Data in Mobile Crowdsensing. IEEE Transactions on Mobile Computing (TMC). Accepted.
  • Wu, W., Wu, Y.*, Lin, W., Zuo, W. (2024) Horizontal Federated Learning: Research Status, System Applications and Open Challenges. Chinese Journal of Computers (in Chinese). Early Access. (横向联邦学习：研究现状、系统应用与挑战. 计算机学报)
  • Peng, P., Lin, W., Wu, W.*, Zhang, H., Peng, S., Wu, Q., & Li, K. (2024) A survey on Computation Offloading in Edge Systems: from the Perspective of Deep Reinforcement Learning Approaches. Computer Science Review. Accepted.
  • Lin, W., Wang, S., Wu, W.*, Li, D., & Zomaya, A. (2024) 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.
  • 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.
  • Liu, B., Chen, R., Lin, W.*, Wu, W.*, Lin, J., & Li. K. (2023) Thermal-Aware Virtual Machine Placement Based on Multi-objective Optimization. Journal of Supercomputing. DOI: 10.1007/s11227-023-05136-z.
  • 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
  • Wu, W., He, L.*, Lin, W., & Mao, R. (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
  • Wu, W., He, L.*, Lin, W., Mao, R., & Jarvis, S. (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. [IEEE Computer Society 2021 Best Paper Award Runner-up]
  • Wu, W., He, L.*, Lin, W. et al. (2020) Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality. IEEE Transactions on Knowledge and Data Engineering (TKDE).DOI: 10.1109/TKDE.2020.3035685.
  • Wu, W., Lin, W.*, He, L., Wu, G., Hsu, C., & Li, K. (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.
  • Huang, T., Lin, W.*, Wu, W, He, L., Li, K., & Zomaya, A.Y. (2020) 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.
  • Lin, W., Shi, F.*, Wu, W., Li, K., Wu, G., & Mohammed, A. (2020). A Taxonomy and Survey of Power Models and Power Modeling for Cloud Servers. ACM Computing Surveys (CSUR). vol. 53, no. 5, pp. 1-41. DOI: 10.1145/3406208.
  • Pang, X., Zhou, Y., Li, P., Lin, W.*, Wu, W*, & Wang J.Z. (2020). A novel syntax-aware automatic graphics code generation with attention-based deep neural network. Journal of Network and Computer Applications. Vol. 161. DOI: 10.1016/j.jnca.2020.102636.
  • Li, P., Li, Z., Pang, X., Wang, H., Lin, W., & Wu, W. (2021). Multi-scale residual denoising GAN model for producing super-resolution CTA images. Journal of Ambient Intelligence and Humanized Computing. Vol.13, pp.1515-1524. DOI: 10.1007/s12652-021-03009-y.
  • Lin, W., Wu, W.* (equal contributions), He, L., & Li, K. (2019). 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). DOI: 10.1109/TSC.2019.2961082.
  • Lin, W.*, Wang, W., Wu, W., Pang X., Liu B., & Zhang Y. (2018). A heuristic task scheduling algorithm based on server power efficiency model in cloud environments. Sustainable Computing: Informatics and Systems, vol. 20, pp. 56-65.
  • Wu, W., Lin, W.*, Hsu C., & He, L. (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.
  • Lin, W.*, Wu, W.*, Wang, H., Wang, J. & Hsu, C. (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.
  • Lin. W.*, Wang, H. & Wu, W. (2018). A Power Monitoring System based on a Multi-component Power Model. International Journal of Grid and High Performance Computing, vol. 10, no.1, pp.16-30.
  • Wu, W., Lin, W.*, & Peng, Z. (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.
  • Lin, W., Wu, W.*, & Wang, J. (2016). A heuristic task scheduling algorithm for heterogeneous virtual clusters. Scientific Programming, vol. 2016, pp. 1-10. DOI:10.1155/2016/7040276.
  • Lin, W., Wu, W.*(2016). Energy consumption measurement and management in cloud computing environment. Ruan Jian Xue Bao/Journal of Software, 27(4), 1026-1041 (in Chinese). DOI: 10.13328/j.cnki.jos.005022. (面向云计算环境的能耗测量和管理方法, 软件学报)
  • 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.

Books & Book Chapters

  • Wu, W., Lin, W., & Li, K. (2023). Energy Efficiency of Servers in Data Centers. in xxx (Ed.) Encyclopedia of Sustainable Technologies, 2nd Edition. Amsterdam, Netherlands: Elsevier. [ISBN: 9780124095489]
  • Lin, W., Wu, W., & Li, K. (May 2021). Chapter 19: Energy Saving Technologies of Servers in Data Centers. Data Center Handbook: Plan, Design, Build, and Operations of a Smart Data Center, 2nd Edition, Hwaiyu Geng, John Wiley & Sons. ISBN: 9781119597506.

Preprints