v1.6.0

Summary

Concrete ML 1.6 includes the following enhancements:

• Latency improvements on large neural networks
• Support for pre-trained tree-based models such as those trained using Federated Learning
• Enhanced collaborative computation
  • Introduction of DataFrame schemas
  • Deployment of logistic regression training

What's Changed

New features

• Enable non-interactive encrypted training for logistic regression (#660) (ec58bca)
• Support pre-trained tree-based models using from_sklearn (5ca282b)
• Add FHE training deployment (#665) (b718629)
• Support approximate rounding to speed up neural networks (9ef890e)
• Allow users to define a schema for dataframe encryption (‘ccd6641’)

Fixes

• Fix fhe-training classes behavior (a88d704)
• Update qgpt2_class.py to fix typo (d376d85)
• Fix post-processing shape mismatches for linear models (#585) (b097022)
• Disable overflow protection in rounding (4db0157)
• Make skorch import fail without error (81de55c)

Improvements

• Replace python release install with setup-python (899b9f1)
• Add support to AvgPool's missing parameters (15a8340)

Resources

• Documentation:

  • Add schema example for encrypted data-frames (#715) (b174509)
  • Add a license FAQ in README (#711) (f937c9f)
  • Update documentation on client / server API (#663) (f864407)
  • Document n_bits for compile torch functions (0306c65)
  • Add examples for the impact of feature scaling on linear models (9252f57)

• Demo & Examples:

  • Add NN-20 and NN-50 deep MLPs for MNIST classification (1b5ce84)