利用种子模型蒸馏实现异构分散式机器非学习

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE CAAI Transactions on Intelligence Technology Pub Date : 2024-01-17 DOI:10.1049/cit2.12281
Guanhua Ye, Tong Chen, Quoc Viet Hung Nguyen, Hongzhi Yin
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引用次数: 0

摘要

由于最近的一些信息安全立法赋予用户无条件被任何训练有素的机器学习模型遗忘的权利,因此个性化物联网服务提供商必须考虑取消学习功能。解除学习用户贡献的最直接方法是从初始状态重新训练模型,但这在频繁提出解除学习请求的高吞吐量应用中并不现实。虽然已经提出了一些机器解除学习框架来加快重新训练过程,但它们无法与分散学习场景相匹配。我们设计了一种名为 "带种子的异构分散式解除学习框架(HDUS)"的分散式解除学习框架,它使用经过提炼的种子模型为所有客户端构建可擦除的集合。此外,该框架与异构设备模型兼容,在实际应用中具有更强的可扩展性。在三个真实世界数据集上进行的广泛实验表明,我们的 HDUS 达到了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Heterogeneous decentralised machine unlearning with seed model distillation

As some recent information security legislation endowed users with unconditional rights to be forgotten by any trained machine learning model, personalised IoT service providers have to put unlearning functionality into their consideration. The most straightforward method to unlearn users' contribution is to retrain the model from the initial state, which is not realistic in high throughput applications with frequent unlearning requests. Though some machine unlearning frameworks have been proposed to speed up the retraining process, they fail to match decentralised learning scenarios. A decentralised unlearning framework called heterogeneous decentralised unlearning framework with seed (HDUS) is designed, which uses distilled seed models to construct erasable ensembles for all clients. Moreover, the framework is compatible with heterogeneous on-device models, representing stronger scalability in real-world applications. Extensive experiments on three real-world datasets show that our HDUS achieves state-of-the-art performance.

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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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