R6 Federated ML application at edge Release Notes

Owned by haihui wang
Mar 17, 2022
1 min read

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