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浙江大学学报(工学版)  2024, Vol. 58 Issue (10): 2062-2068    DOI: 10.3785/j.issn.1008-973X.2024.10.009
计算机与控制工程     
基于双向自举蒸馏的异质云-端医疗对话联邦
刘宇鹏(),林明豪,张江,姚登举
哈尔滨理工大学 计算机科学与技术学院,黑龙江 哈尔滨 150080
Heterogeneous cloud-end medical dialogue federation based on bi-directional bootstrapping distillation
Yupeng LIU(),Minghao LIN,Jiang ZHANG,Dengju YAO
School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
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摘要:

医疗对话场景下的数据/模型异质、数据类型不同,为此提出新的联邦学习方法. 云模型和端模型以相互自举蒸馏的方式进行知识递进传递. 端到云的自举蒸馏过程为多教师-单学生模式,知识被从多个局部模型蒸馏统一到全局模型;云到端的自举蒸馏过程为单教师-多学生模式,知识被从全局模型蒸馏回多个局部模型. 在医疗对话ReMeDi和MedDG数据集上,所提方法与经典基线相比通过文本生成指标评价获得了显著提高,训练速度有所提升.

关键词: 自举蒸馏异质数据异质模型结构正则医疗对话    
Abstract:

A new federated learning method was proposed in the medical dialogue scene for the heterogeneous data/models and different types of data. The cloud model and the end model transferred knowledge by mutual bootstrapping distillation. The end-to-cloud bootstrapping distillation process was a multi-teacher-single-student model, and knowledge was distilled from multiple local models to a global model. The cloud-to-end bootstrapping distillation process was a single-teacher-multi-student model, and knowledge was distilled from the global model back to multiple local models. On the medical dialogue ReMeDi and MedDG data sets, the proposed method is significantly improved compared with the classical baseline by the text generation evaluation criterion, and the training speed has also been improved.

Key words: bootstrapping distillation    heterogenous data    heterogenous model    structure regularization    medical dialogue
收稿日期: 2023-07-29 出版日期: 2024-09-27
CLC:  TP 393  
基金资助: 国家自然科学基金资助项目(61300115, 62172128).
作者简介: 刘宇鹏(1978—),男,博士,教授,从事自然语言处理研究. orcid.org/0000-0002-8437-6894. E-mail:flyeaglelyp@hrbust.edu.cn
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引用本文:

刘宇鹏,林明豪,张江,姚登举. 基于双向自举蒸馏的异质云-端医疗对话联邦[J]. 浙江大学学报(工学版), 2024, 58(10): 2062-2068.

Yupeng LIU,Minghao LIN,Jiang ZHANG,Dengju YAO. Heterogeneous cloud-end medical dialogue federation based on bi-directional bootstrapping distillation. Journal of ZheJiang University (Engineering Science), 2024, 58(10): 2062-2068.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.10.009        https://www.zjujournals.com/eng/CN/Y2024/V58/I10/2062

图 1  基于双向自举蒸馏的联邦学习方法
方法ReMeDiMedDG
BLEU-1BLEU-4ROGUE-1ROGUE-2Distinct-1Distinct-2BLEU-1BLEU-4ROGUE-1ROGUE-2Distinct-1Distinct-2
中心化训练27.866.5950.3632.250.728.5930.4714.2153.9735.730.8710.92
FedAvg18.374.8338.6422.450.505.3219.899.6239.7125.870.587.06
FedMD21.415.7941.9226.930.637.5423.7411.8443.7630.210.639.14
FedDF21.685.4640.4526.640.628.0624.2611.0343.8929.510.779.25
FedGen24.086.3842.6427.680.657.9226.1013.0546.1732.040.699.87
FedBiD25.016.3245.7628.190.688.3226.7513.1646.8331.630.789.98
表 1  不同联邦学习方法在2个数据集上的性能比较
图 2  同质数据下的模型表现
图 3  异质数据下的模型表现
客户端模型层数隐层维度np/106
1GPT-2-small12768117
2GPT-2241024345
3BART-base12768130
4BART241024374
表 2  各客户端的模型参数
图 4  各客户端的模型性能变化
数据集BLEU
T1=1T1=10T1=20T1=30T2=0.1T2=1.0T2=2.0T2=5.0
ReMeDi11.8514.3715.7113.9212.1715.7114.8512.46
MedDG13.7918.0219.9117.6815.8819.9118.3816.25
表 3  温度对模型性能的影响
模型层数隐藏层维度np/106BLEU
GPT-2-small1276811716.75
GPT-224102434519.91
GPT-2-large36128076220.62
GPT-2-max481600154222.03
表 4  不同模型参数对模型性能的影响
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