A Survey for Federated Learning Evaluations: Goals and Measures.
IEEE Trans. Knowl. Data Eng., October, 2024
PackVFL: Efficient HE Packing for Vertical Federated Learning.
CoRR, 2024
Efficient Decentralized Federated Singular Vector Decomposition.
Proceedings of the 2024 USENIX Annual Technical Conference, 2024
UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services.
Proceedings of the IEEE International Conference on Software Services Engineering, 2024
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework.
IEEE Trans. Knowl. Data Eng., April, 2023
UCTB: An Urban Computing Tool Box for Spatiotemporal Crowd Flow Prediction.
CoRR, 2023
A Survey on Vertical Federated Learning: From a Layered Perspective.
CoRR, 2023
Efficient Federated Matrix Factorization Against Inference Attacks.
ACM Trans. Intell. Syst. Technol., 2022
Sphinx: Enabling Privacy-Preserving Online Learning over the Cloud.
Proceedings of the 43rd IEEE Symposium on Security and Privacy, 2022
Practical Lossless Federated Singular Vector Decomposition over Billion-Scale Data.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
Practical and Secure Federated Recommendation with Personalized Mask.
Proceedings of the Trustworthy Federated Learning - First International Workshop, 2022
Secure Forward Aggregation for Vertical Federated Neural Networks.
Proceedings of the Trustworthy Federated Learning - First International Workshop, 2022
Secure Federated Matrix Factorization.
IEEE Intell. Syst., 2021
Aegis: A Trusted, Automatic and Accurate Verification Framework for Vertical Federated Learning.
CoRR, 2021
Federated Singular Vector Decomposition.
CoRR, 2021
FedEval: A Benchmark System with a Comprehensive Evaluation Model for Federated Learning.
CoRR, 2020
Exploring the Generalizability of Spatio-Temporal Crowd Flow Prediction: Meta-Modeling and an Analytic Framework.
CoRR, 2020
Bike flow prediction with multi-graph convolutional networks.
Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2018