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Release v1.11.0
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@mgqa34 mgqa34 released this 13 Apr 08:53
· 2379 commits to master since this release
v1.11.0
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Major Features and Improvements

FederatedML

  • Support FATE-LLM (Federated Large Language Models)
    • Integration of LLM for federated learning: BERT, ALBERT, RoBERTa, GPT-2, BART, DeBERTa, and DistilBERT. Please note that if using such pretrain-models, compliance with their licenses is needed.
    • Integration of Parameter-efficient tuning methods for federated learning: Bottleneck Adapters (including Houlsby, Pfeiffer, Parallel schemes), Invertible Adapters, LoRA, IA3, and Compacter.
    • Improved Homo Federated Trainer class, allowing CUDA device specification and DataParallel acceleration for multi-GPU devices.
    • TokenizerDataset feature upgrade, better adaptation to HuggingFace Tokenizer.

Bug-Fix

  • Fix inconsistent bin_num display of Hetero Feature Binning for data contains missing value
  • Fix inconsistency in transforming data for transforming selected columns of Hetero Feature Binning When using ModelLoader
  • Fix exclusive_data_type not valid in DataTransform when meta for input data is missing
  • Fix weighted loss calculation and feature importance display issues in Tree-Based models
  • Fix sample id display of NN
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