Add README with paper summary and results

README.md

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+# 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:
+1. 🎯 **Contrastive Forgetting Loss (CFL)** — Repels forget-set embeddings from class centroids while anchoring retain-set representations
+2. 📐 **Lipschitz Feature Sensitivity** — Minimizes model responsiveness to target features via perturbation-based sensitivity
+3. ✅ **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.