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Release v1.10.0
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@mgqa34 mgqa34 released this 29 Dec 10:38
· 2444 commits to master since this release
v1.10.0
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Major Features and Improvements

FederatedML

  • Renewed Homo NN: PyTorch-based, support flexible model building:
    • Support user access to complex self-defined PyTorch models or ready-to-use PyTorch models such as DeepFM, ResNet, BERT, Yolo
    • Support various data set types, may build data set based on PyTorch Dataset
    • User-defined training loss
    • User-defined training process: user-defined aggregation algorithm for client and server
    • Provide API for developing Aggregator
  • Upgraded Hetero NN: support flexible model building and various data set types:
    • more flexible pytorch top/bottom model customization; provide access to industry approved PyTorch models
    • User-defined training loss
    • Support various data set types, may build data set based on PyTorch Dataset
  • Renewed Homo-federated framework with support for all current homo models, including Homo NN, Homo LR,Homo SecureBoost,
    Homo Feature Binning, and Hetero KMeans. This provides smoother algorithm customization and development experience
  • Semi-Supervised Algorithm Positive Unlabeled Learning
  • Hetero LR & Hetero SecureBoost now supports Intel IPCL
  • Intersection support Multi-host Elliptic-curve-based PSI
  • Intersection may compute Multi-host Secure PSI Cardinality
  • Hetero Feature Optimal Binning now record & show Gini/KS/Chi-Square metrics
  • Host may load Hetero Binning model with WOE score through Model Loader
  • Hetero Feature Binning support binning by user-provided split points
  • Sampler support weighted sampling by instance weight

Fate-Client

  • Flow CLI adds min-test options
  • Pipeline adds data-bind API, useful for local development
  • Pipeline may reconfigure role/model_id/model_version, switching party_id for prediction task
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