---
license: cc-by-nc-4.0
task_categories:
- text-classification
- question-answering
- summarization
- text-retrieval
- text-generation
language:
- zh
tags:
- legal
- privacy
- federated-learning
---

# FedPII Dataset

This repository contains the dataset for the paper [Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models](https://huggingface.co/papers/2506.06060).

FedPII is a Chinese legal-domain dataset with fine-grained PII (Personally Identifiable Information) annotations consistent with CPIS, GDPR, and CCPA standards. It is designed to evaluate privacy risks and cross-client data leakage in Federated Large Language Models (FedLLMs).

- **GitHub Repository:** [SMILELab-FL/FedPII](https://github.com/SMILELab-FL/FedPII)

## Dataset Description

The dataset consists of 16,194 samples across various legal-related tasks. It includes fine-grained PII annotations (such as names, addresses, and birthdays) used to assess how easily private information can be recovered during federated learning.

### Tasks and Statistics

| Task | Description | Split | Samples | With PII |
| :--- | :--- | :--- | :--- | :--- |
| `leg_case_cls` | Legal Case Classification | Train/Test | 4,028 | 1,019 |
| `jud_sum` | Judicial Summarization | Train/Test | 2,651 | 2,650 |
| `sim_case_match` | Similar Case Matching | Train/Test | 3,773 | 3,771 |
| `jud_read_compre` | Judicial Reading Comprehension | Train/Test | 3,498 | 3,493 |
| `exam` | Legal Exam | Train/Test | 2,244 | 700 |

**Total Samples:** 16,194 (14,575 Train / 1,619 Test).

## Citation

```bibtex
@inproceedings{hu-etal-2025-simple,
    title = "Simple Yet Effective: Extracting Private Data Across Clients in Federated Fine-Tuning of Large Language Models",
    author = "Hu, Yingqi  and
      Zhang, Zhuo  and
      Zhang, Jingyuan  and
      Wang, Jinghua  and
      Wang, Qifan  and
      Qu, Lizhen  and
      Xu, Zenglin",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.findings-ijcnlp.113/",
    pages = "1808--1827",
    ISBN = "979-8-89176-303-6",
}
```

## License

This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).