稀疏的知识共享 (SKS) 保护隐私的域增量扣押检测检测
Jiayu An1,2, Ruimin Peng1,2, Zhenbang Du1,2
1Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.
Journal of neural engineering
|February 24, 2025
概括
我们介绍了Sparse知识共享 (SKS),这是一个用于隐私保护领域增量学习的新方法,用于发作检测. SKS有效地从新患者数据中学习,同时保护隐私并防止模型退化.
科学领域:
- 神经学 神经学
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 影响全球数以百万计的人,需要基于EEG的精确发作检测来诊断和监测.
- 目前的发作检测模型通常是针对患者的,因为数据分布的变化,需要个性化培训.
- 保护隐私的域增量学习 (PP-DIL) 解决了来自不同患者域的顺序学习,同时保护数据隐私.
研究的目的:
- 开发一种保护隐私的域增量学习方法,用于基于EEG的发作检测.
- 为了应对灾难性遗忘,隐私保护和域名分配转移的挑战,PP-DIL.
- 提出一种没有排练的方法,可以平衡模型的可塑性和稳定性.
主要方法:
- 为PP-DIL提出的稀疏知识共享 (SKS) 方法.
- 利用欧几里德对齐来标准化跨领域的数据.
- 实现了SKS的自适应修剪,以创建域特定和共享的参数.
- 纳入监督对比学习以改善特征歧视.
主要成果:
- 在保护隐私的域增量学习任务中,SKS表现出卓越的表现.
- 在两个公共扣押数据集上进行的实验验证实了SKS的有效性.
- 该方法在学习新信息 (可塑性) 和保留旧信息 (稳定性) 之间实现了有利的平衡.
结论:
- SKS提供了一种有效的无排练和保护隐私的解决方案,用于在发作检测中进行顺序学习.
- 该方法成功地减轻了灾难性遗忘,并处理了域名转移.
- SKS为个性化,对隐私有意识的扣押监控系统提供了一个强大的框架.
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