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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 (Akraino new)

    • 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

    dependencies of the release (upstream version, patches)

         still the same

    differences from previous version

        add several new modules in FATE

              accelerate the computing for hetero, horizontal, and feature binning method

               

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