BriefGPT.xyz
Feb, 2020
通过本地适配挽救联合学习
Salvaging Federated Learning by Local Adaptation
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Tao Yu, Eugene Bagdasaryan, Vitaly Shmatikov
TL;DR
针对联邦学习中参与者局部利益与数据准确度不匹配的问题,本文提出并比较了三种本地适应技术,并指出差分隐私和鲁棒聚合恶化了联邦模型的准确性。这三种技术分别为:微调、多任务学习和知识蒸馏。我们的实验结果表明,所有参与者都从本地适应中受益,并且本地模型表现不佳的参与者通过传统联邦方式得到了大幅提升。
Abstract
federated learning
(FL) is a heavily promoted approach for training
ml models
on sensitive data, e.g., text typed by users on their smartphones. FL is expressly designed for training on data that are unbalanced a
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