| --- |
| 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/). |
