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Major Features and Improvements

FATE-ARCH

• Support EggRoll 2.4.0
• Support Spark-Local Computing Engine
• Support Hive Storage
• Support LocalFS Storage for Spark-Local Computing Engine
• Optimizing the API interface for Storage session and table
• Simplified the API interface for Session, remove backend and workmode parameters
• Heterogeneous Engine Support: Federation between Spark-Local and Spark-Cluster
• Computing Engine, Storage Engine, Federation Engine are set in conf/service_conf.yaml when FATE is deployed

FederatedML

• Optimized Hetero-SecureBoost: with gradient packing、cipher compressing, and sparse point statistics optimization, 4x+ faster
• Homo-SecureBoost supports memory-based histogram computation for more efficient tree building, 5x+ faster
• Optimized RSA Intersect with CRT optimization, 3x+ faster
• New two-party Hetero Logistic Regression Algorithm: mixed protocol of HE and MPC, without a trusted third party
• Support data with match-id, separating match id and sample id
• New DH Intersect based on PH Key-exchange protocol
• Intersect support cardinality estimation
• Intersect adds optionally preprocessing step
• RSA and DH Intersect support cache
• New Feature Imputation module: can apply arbitrary imputation method to each column
• New Label Transform module: transform categorical label values
• Homo-LR, Homo-SecureBoost, Homo-NN now can convert models into sklearn、lightgbm、torch & tf-keras framework
• Hetero Feature Binning supports multi-class task, higher efficiency with label packing
• Hetero Feature Selection support multi-class iv filter
• Secure Information Retrieval supports multi-column retrieval
• Major training algorithms support warm-start and checkpoint : Homo & Hetero LR, Homo & Hetero-SecureBoost, Homo & Hetero NN
• Optimized Pailler addition operation, several times faster, Hetero-SecureBoost with Paillier speed up 2x+

Fate-Client

• Pipeline supports uploading match id functionality
• Pipeline supports homo model conversion
• Pipeline supports model push to FATE-Serving
• Pipeline supports running jobs with specified FATE version

FATE-Test

• Integrate FederatedML unittest
• Support for uploading image data
• Big data generation using storage interface, optimized generation logic
• Support for historical data comparison
• cache_deps and model_loader_deps support
• Run DSL Testsuite with specified FATE version