Release v1.10.0

By downloading, installing or using the software, you accept and agree to be bound by all of the terms and conditions of the LICENSE and DISCLAIMER.

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