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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.