Biography

Di Chai is a Ph.D. student of computer science and engineering at the Hong Kong University of Science and Technology. He is under the supervision of Prof. Kai Chen and Prof. Qiang Yang. His research interests are intelligent, secure, and high-performance distributed computation systems.

Email: dchai[at]cse[dot]ust[dot]hk

Curriculum Vitae

Research Statement

Research Projects

FedEval (Federated Learning Evaluation System)

FedEval is a federated learning benchmark system with a comprehensive evaluation model, which defines three evaluation goals and the corresponding measures for the FL systems: utility, efficiency, and security & privacy.

Di Chai*, Leye Wang*, Liu Yang, Junxue Zhang, Kai Chen, & Qiang Yang. “A Survey for Federated Learning Evaluations: Goals and Measures”. IEEE TKDE accepted (2024). (*Equal Contribution) [Paper] [Code]

UCTB (Urban Computing ToolBox)

Urban Computing ToolBox is a package providing spatial-temporal predicting models. It contains both conventional models and state-of-the-art deep learning models. Besides, benchmark datasets built from open data are included. More details in:

Leye Wang, Di Chai, Xuanzhe Liu, Liyue Chen, & Kai Chen. “Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework”. IEEE TKDE accepted (2021). [Paper] [Code]
陈李越，柴迪，王乐业. “UCTB: 时空人群流动预测工具箱”. 计算机科学与探索 (2021). [Paper]

Publications

Conference Paper

Di Chai, Junxue Zhang, Liu Yang, Yilun Jin, Leye Wang, Kai Chen, & Qiang Yang. “Efficient Decentralized Federated Singular Vector Decomposition”. USENIX ATC’24 Accepted. [Paper] [Code]
Di Chai, Leye Wang, Junxue Zhang, Liu Yang, Shuowei Cai, Kai Chen, & Qiang Yang. “Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data”. KDD 2022, research track. [Paper] [Code]
Han Tian, Chaoliang Zeng, Zhenghang Ren, Di Chai, Junxue Zhang, Kai Chen, Qiang Yang, “Sphinx: Enabling privacy-preserving online learning over the cloud”. IEEE S&P 2022. [Paper]
Di Chai, Leye Wang, Qiang Yang. “Bike Flow Prediction with Multi-Graph Convolutional Networks”. SIGSPATIAL/GIS 2018: 397-400 (2018). [Paper] [Code] [Google Scholar 300+ citations]

Journal Paper

Di Chai*, Leye Wang*, Liu Yang, Junxue Zhang, Kai Chen, & Qiang Yang. “A Survey for Federated Learning Evaluations: Goals and Measures”. IEEE TKDE accepted (2024). (*Equal Contribution) [Paper] [Code]
Di Chai, Leye Wang, Kai Chen, Qiang Yang, “Secure Federated Matrix Factorization”. IEEE Intelligent Systems, 36(5): 11-20 (2021). [Paper] [Code] [Google Scholar 300+ citations]
Di Chai, Leye Wang, Kai Chen, Qiang Yang, “Efficient Federated Matrix Factorization against Inference Attacks”. ACM TIST (2021). [Paper]
Leye Wang, Di Chai, Xuanzhe Liu, Liyue Chen, & Kai Chen. “Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework”. IEEE TKDE, accepted (2021). [Paper] [Code]

Workshop Paper

Liu Yang, Junxue Zhang, Di Chai, Leye Wang, Kun Guo, Kai Chen, Qiang Yang, “Practical and secure federated recommendation with personalized mask”. International Workshop on Trustworthy Federated Learning (2022). [Paper]
Shuowei Cai, Di Chai, Liu Yang, Junxue Zhang, Yilun Jin, Leye Wang, Kun Guo, Kai Chen, “Secure forward aggregation for vertical federated neural networks”. International Workshop on Trustworthy Federated Learning (2022). [Paper]
Cengguang Zhang, Junxue Zhang, Di Chai, Kai Chen, “Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning”. IJCAI FL-Workshop (2021), Best Application Award. [Paper]