Release v1.8.0
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Major Features and Improvements
FederatedML
- Add non-coordinated-version Hetero Linear Regression, based on integrated Hetero GLM framework, with mixed protocol of HE and SPDZ
- Homo LR support one-vs-rest
- Add SecureBoost-MO algorithm to speed up multi-class classification of Hetero & Homo SecureBoost, 1.5x-5x faster
- Optimize Hetero SecureBoost Predict Transmission Data Size,reduce 75% bandwidth consumption if tree's max depth is small
- Speed up DH Intersection implementation, 30%+ faster
- Optimized Quantile Binning gk-summary structure & split point query,20%+ faster, less memory cost
- Support weighted training in non-coordinated Hetero Logistic Regression & Linear Regression
- Merge Hetero FastSecureBoost into Hetero SecureBoost as a boosting strategy option
Fate-ARCH
- Adjustable task_cores for standalone FATE
- Enable Eggroll option to make computing output "IN_MEMORY" by default
Fate-Test
- Include Paillier encryption performance evaluation
- Include SPDZ performance evaluation
- Optimized testsuite printout
- Include examples data upload and mnist download
- Provide pipeline to dsl convert tools
Bug-Fix
- Fix bug for SPDZ when using default q_filed
- Fix multiple get problem of SPDZ
- Fix bugs of recursive-query homo feature binning
- Fix homo_nn's model aggregation problem
- Fix bug for hetero feature selection when using federated filter but some party's feature is empty.