forgetnet 0.1.5

A package for applying differential privacy to model training using block-level gradient shuffling

🛡️ ForgetNet: Differentially Private Block-wise Gradient Shuffle for Deep Learning 🧠

ForgetNet introduces a novel privacy-preserving technique for deep learning: Differentially Private Block-wise Gradient Shuffle (DP-BloGS). 🔒🔀

🌟 Features

• 🚀 Fast training times, close to non-private training
• 🎯 Competitive privacy guarantees compared to DP-SGD
• 📊 Better privacy-utility trade-off in many scenarios
• 🏋️ Scalable to large models (tested up to 1.1 billion parameters)
• 🧮 Parameter-wise privacy budget allocation

📦 Installation

pip install forgetnet

🚀 Quick Start

from forgetnet import DPBloGSTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load your model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Initialize the DP-BloGS trainer
trainer = DPBloGSTrainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer,
    target_epsilon=1.0,
    delta=1e-5,
    clip_value=1.0
)

# Train your model with privacy guarantees
trainer.train()

📚 How It Works

DP-BloGS introduces a probabilistic approach to gradient noise through block-wise shuffling:

1. 📊 Divide gradients into blocks
2. 🔀 Shuffle blocks randomly
3. 📏 Apply parameter-specific block sizes
4. ✂️ Use batch layer clipping
5. 🧮 Accumulate gradients

This combination allows for fast training while maintaining strong privacy guarantees!

📈 Performance

DP-BloGS has been tested on various model architectures, including:

• GPT-2 (124M)
• BERT (110M)
• OPT (350M)
• BLOOM (560M)
• TinyLlama (1.1B)

Results show competitive or better performance compared to DP-SGD in terms of:

• 🏃‍♂️ Training speed
• 🎭 Privacy guarantees
• 📊 Model utility

📄 Citation

If you use ForgetNet in your research, please cite my paper:

@article{zagardo2024dpblogs,
  title={Differentially Private Block-wise Gradient Shuffle for Deep Learning},
  author={Zagardo, David},
  journal={arXiv preprint arXiv:2024.XXXXX},
  year={2024}
}

🤝 Contributing

We welcome contributions! Please see my CONTRIBUTING.md for details on how to get started.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

We thank the open-source community and the authors of the papers cited in our work for their valuable contributions to the field of privacy-preserving machine learning.

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Built with 🧠 by David Zagardo