Verril-Learn 🧠✂️

A Surgical Machine Unlearning Library for Edge Devices

🎯 What is Verril-Learn?

Verril-Learn is a novel machine unlearning library designed specifically for edge devices. It enables you to surgically remove the influence of specific data points from a trained model without full retraining.

Key Features

• 🔬 Surgical Unlearning: Remove specific data influence using Fisher Information + Gradient Ascent
• 🎭 Data Poisoning Simulation: Built-in tools to simulate and study data attacks
• 📱 Edge-Device Optimized: Lightweight algorithms suitable for resource-constrained environments
• 🔒 Privacy-Compliant: Implements concepts aligned with GDPR's "Right to be Forgotten"

📦 Installation

pip install -e .

Or install dependencies directly:

pip install -r requirements.txt

🚀 Quick Start

from verril_learn import get_poisoned_mnist, SimpleCNN, surgical_unlearn

# Step 1: Load poisoned MNIST (10% of '7's mislabeled as '1's)
train_loader, test_loader, poison_indices = get_poisoned_mnist(poison_ratio=0.1)

# Step 2: Train your model (standard PyTorch training loop)
model = SimpleCNN()
# ... train the model ...

# Step 3: Surgically unlearn the poisoned data
unlearned_model = surgical_unlearn(
    model=model,
    poisoned_data=poisoned_loader,
    retain_data=clean_loader,
    fisher_weight=0.1,
    unlearn_lr=0.001,
    unlearn_steps=10
)

🧪 The Algorithm

1. Fisher Information Matrix (Diagonal Approximation)

Identifies which weights are critical for the retain data:

F_ii = E[(∂L/∂θ_i)²]

2. Gradient Ascent on Poisoned Data

Maximizes the loss to make the model "forget":

θ = θ + α * ∇L(θ; D_poison)

3. Fisher-Weighted Regularization

Prevents catastrophic forgetting by penalizing large changes to critical weights:

L_total = -L_poison + λ * Σ F_ii * (θ_i - θ_i^original)²

📁 Project Structure

verril-learn/
├── setup.py              # Package configuration
├── requirements.txt      # Dependencies
├── README.md             # This file
└── verril_learn/
    ├── __init__.py       # Public API
    ├── data_loader.py    # MNIST loading + poisoning
    ├── model.py          # CNN architecture
    └── core.py           # Surgical unlearning algorithm

📚 References

• Machine Unlearning (Bourtoule et al., 2021)
• Fisher Information for Deep Learning

📄 License

MIT License - See LICENSE file for details.

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