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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