FEDLEGAL: The First Real-World Federated Learning Benchmark for Legal NLP
Zhuo Zhang, Xiangjing Hu, Jingyuan Zhang, Yating Zhang, Hui Wang, Lizhen Qu, Zenglin Xu
Abstract
The inevitable private information in legal data necessitates legal artificial intelligence to study privacy-preserving and decentralized learning methods. Federated learning (FL) has merged as a promising technique for multiple participants to collaboratively train a shared model while efficiently protecting the sensitive data of participants. However, to the best of our knowledge, there is no work on applying FL to legal NLP. To fill this gap, this paper presents the first real-world FL benchmark for legal NLP, coined FEDLEGAL, which comprises five legal NLP tasks and one privacy task based on the data from Chinese courts. Based on the extensive experiments on these datasets, our results show that FL faces new challenges in terms of real-world non-IID data. The benchmark also encourages researchers to investigate privacy protection using real-world data in the FL setting, as well as deploying models in resource-constrained scenarios. The code and datasets of FEDLEGAL are available here.
BibTeX
@inproceedings{zhang-etal-2023-fedlegal,
title = "{FEDLEGAL}: The First Real-World Federated Learning Benchmark for Legal {NLP}",
author = "Zhang, Zhuo and
Hu, Xiangjing and
Zhang, Jingyuan and
Zhang, Yating and
Wang, Hui and
Qu, Lizhen and
Xu, Zenglin",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.193/",
doi = "10.18653/v1/2023.acl-long.193",
pages = "3492--3507"
}