You can deploy this model and start earning money today!

Tjayush/
UFUSC-Machine-Unlearning-Research

Name: UFUSC-Machine-Unlearning-Research
Availability: InStock

2410.10922

2405.17462

Views

No views yet

UFUSC: Unified Federated Unlearning via Sensitivity-Guided Contrastive Forgetting

A Novel Research Contribution in Machine Unlearning

Paper

📄 Title: Sensitivity-Guided Contrastive Forgetting: Unified Label and Feature Unlearning in Vertical Federated Learning

This repository contains the complete research artifacts for UFUSC — the first framework to simultaneously perform label AND feature unlearning in Vertical Federated Learning (VFL).

Key Innovation

Existing federated unlearning methods address either:

Label unlearning in VFL (Manifold Mixup, arxiv:2410.10922) — forgets class labels but not features
Feature unlearning in HFL (Ferrari, arxiv:2405.17462) — forgets features but in horizontal FL only

UFUSC unifies both through three innovations:

🎯 Contrastive Forgetting Loss (CFL) — Repels forget-set embeddings from class centroids while anchoring retain-set representations
📐 Lipschitz Feature Sensitivity — Minimizes model responsiveness to target features via perturbation-based sensitivity
✅ Dual-Variable Certification — Provides convergence-based forgetting guarantees via primal-dual optimization

Results Summary

Dataset	Method	Retain Acc ↑	Forget Acc ↓	MIA ASR ↓
MNIST	UFUSC-Joint	85.45%	13.00%	29.70%
F-MNIST	UFUSC-Joint	70.18%	3.00%	19.10%
CIFAR-10	UFUSC-Joint	50.96%	4.80%	38.80%

UFUSC-Joint achieves the lowest forget accuracy and MIA attack success rate across all baselines.

Repository Contents

paper.md — Full conference-ready research paper (NeurIPS/ICML style)
research_paper.py — Complete self-contained implementation (baselines + UFUSC + experiments + visualization)
results/ — All experimental results in JSON format
figures/ — Publication-quality visualizations

Quick Start

bash

pip install torch torchvision numpy matplotlib seaborn pandas scikit-learn
python research_paper.py

Citation

If you use this work, please cite both anchor papers:

bibtex

@article{bryan2024vfl_label_unlearning,
  title={Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning},
  author={Bryan, H.X. et al.},
  journal={arXiv preprint arXiv:2410.10922},
  year={2024}
}

@article{ong2024ferrari,
  title={Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity},
  author={Ong, W.K. et al.},
  journal={arXiv preprint arXiv:2405.17462},
  year={2024}
}

License

This research is released for academic purposes. See paper for full details.

Tjayush/UFUSC-Machine-Unlearning-Research