Release v2.0.0

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Major Features and Improvements

• Adapt to fate-v2.0 framework:
  • Migrate parameter-efficient fine-tuning training methods and models.
  • Migrate Standard Offsite-Tuning and Extended Offsite-Tuning（Federated Offsite-Tuning+)
  • Newly trainer，dataset, data_processing function design
• New FedKSeed Federated Tuning Algorithm: train large language models in a federated learning setting with extremely low communication cost

Release v1.3.0

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Major Features and Improvements

• FTL-LLM（Fedrated Learning + Transfer Learning + LLM）
  • Standard Offsite-Tuning and Extended Offsite-Tuning（Federated Offsite-Tuning+）now supported
  • Framework available for Emulator and Adapter development
  • New Offsite-Tuning Trainer introduced
  • Includes built-in models such as GPT-2 family, Llama7b, and Bloom family
• FedIPR
  • Introduced WatermarkDataset as the foundational dataset class for backdoor-based watermarks
  • Added SignConv and SignLayerNorm blocks for feature-based watermark models
  • New FedIPR Trainer available
  • Built-in models with feature-based watermarks include Alexnet, Resnet18, DistilBert, and GPT2
• More models support parameter-efficient fine-tuning: ChatGLM2-6B and Bloom-7B1

Release v1.2.0

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

• Support Federated Training of LLaMA-7B with parameter-efficient fine-tuning.

Release v1.1.0

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

• Support Federated Training of ChatGLM-6B with parameter-efficient fine-tuning adapters: like Lora and P-Tuning V2 etc.
• Integration of peft, which support many parameter-efficient adapters.