@inproceedings{sui-etal-2020-feded,
title = "{F}ed{ED}: Federated Learning via Ensemble Distillation for Medical Relation Extraction",
author = "Sui, Dianbo and
Chen, Yubo and
Zhao, Jun and
Jia, Yantao and
Xie, Yuantao and
Sun, Weijian",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.165/",
doi = "10.18653/v1/2020.emnlp-main.165",
pages = "2118--2128",
abstract = "Unlike other domains, medical texts are inevitably accompanied by private information, so sharing or copying these texts is strictly restricted. However, training a medical relation extraction model requires collecting these privacy-sensitive texts and storing them on one machine, which comes in conflict with privacy protection. In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged. Though federated learning has distinct advantages in privacy protection, it suffers from the communication bottleneck, which is mainly caused by the need to upload cumbersome local parameters. To overcome this bottleneck, we leverage a strategy based on knowledge distillation. Such a strategy uses the uploaded predictions of ensemble local models to train the central model without requiring uploading local parameters. Experiments on three publicly available medical relation extraction datasets demonstrate the effectiveness of our method."
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%0 Conference Proceedings
%T FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction
%A Sui, Dianbo
%A Chen, Yubo
%A Zhao, Jun
%A Jia, Yantao
%A Xie, Yuantao
%A Sun, Weijian
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sui-etal-2020-feded
%X Unlike other domains, medical texts are inevitably accompanied by private information, so sharing or copying these texts is strictly restricted. However, training a medical relation extraction model requires collecting these privacy-sensitive texts and storing them on one machine, which comes in conflict with privacy protection. In this paper, we propose a privacy-preserving medical relation extraction model based on federated learning, which enables training a central model with no single piece of private local data being shared or exchanged. Though federated learning has distinct advantages in privacy protection, it suffers from the communication bottleneck, which is mainly caused by the need to upload cumbersome local parameters. To overcome this bottleneck, we leverage a strategy based on knowledge distillation. Such a strategy uses the uploaded predictions of ensemble local models to train the central model without requiring uploading local parameters. Experiments on three publicly available medical relation extraction datasets demonstrate the effectiveness of our method.
%R 10.18653/v1/2020.emnlp-main.165
%U https://aclanthology.org/2020.emnlp-main.165/
%U https://doi.org/10.18653/v1/2020.emnlp-main.165
%P 2118-2128
Markdown (Informal)
[FedED: Federated Learning via Ensemble Distillation for Medical Relation Extraction](https://aclanthology.org/2020.emnlp-main.165/) (Sui et al., EMNLP 2020)
ACL