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R5 Federated ML application at edge Release Notes

R5 Federated ML application at edge Release Notes

Aug 22, 2021

Release Notes for the <Federated ML Blue Print>l

  • Summary

    This document provides the release note for federated ml applicatons at edge.

     

    what is released 

    components of the release

    • Hetero SecureBoost: more efficient computation with GOSS, histogram subtraction, cipher compression, 2-4x faster

    • Hetero GLM: improved communication efficiency, adjustable floating point precision, 2x faster

    • Hetero NN: adjustable floating point precision, support SelectiveBackPropagation and dropOut on interaction layer, 2x faster

    • Hetero Feature Binning: improved algorithm with cipher compression, 2x faster

    • Intersect: add split calculation option and adjustable random base fraction, 30% faster

    • Homo NN: restructure torch backend and enhanced grammar; train and predict with raw image data

    • Intersect supports SM3 hashing method

    • Hetero SecureBoost: L1 penalty & adjustable min_child_weight to prevent overfitting

    • NEW SecureBoost Transformer: feature engineering module that encodes instances with leaf nodes from SecureBoost model

    • Hetero Pearson: support local VIF computation

    • Hetero Feature Selection: support selection based on VIF and Pearson

    • NEW Homo Feature Binning: support virtual/recursive binning strategy

    • NEW Sample Weight: set sample weights based on label or from feature column, Hetero GLM & Hetero SecureBoost support weighted training

    • NEW Data Transformer: case-insensitive on data schema

    • Local Baseline supports prediction task

    • Cross Validation: output fold split history

    • Evaluation: add multi-result-unfold option which unfolds multi-classification evaluation result to several binary evaluation results in a one-vs-rest manner