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Major Features and Improvements

FederatedML

• Add elliptic curve based PSI algorithm, which allows 128-bit secure-level intersection of billion samples, 20x faster than RSA protocol under the same security level
• Support accurate intersection cardinality calculation
• Support for multi-column ID data; user may specify id column for PSI intersection and subsequent modeling usage
• Hetero NN supports torch backend and supports complex layers such as LSTM
• Add CoAE label reinforcement mechanism for vertical federated neural network
• Hetero NN supports multi-host modeling scenarios
• HeteroSecureBoost supports merging sub-models from all parties and exporting the merged model into lightgbm or PMML format
• HeteroLR and HeteroSSHELR support merging sub-models from all parties and exporting the merged model into sklearn or PMML format
• HeteroFeatureSelection supports anonymous feature selection
• Label Encoder adds automatic label type inference
• 10x faster local VIF computation in HeteroPearson, with added support for computing local VIF on linearly dependent columns
• Optimized feature engineering column processing logic
• HeteroFeatureBinning supports calculation of IV and WOE values during prediction
• Renewed feature anonymous generation logic

FATE-ARCH

• Support python3.8+
• Support Spark 3x
• Renewed Federation module, RabbitMQ and Pulsar support client transmission mode
• Support Standalone, Spark, EggRoll heterogeneous computing engine interconnection

Fate-Client

• PipeLine adds timeout retry mechanism
• Pipeline's get_output_data API now may return component output data in DataFrame-typed format