在联合学习中通过异常体现的异常体现进行分布外检测
1Department of Computer Science and Engineering, Inha University, Incheon, South Korea.
概括
在联合学习 (FL) 中,异常值暴露 (OE) 由于数据限制而具有挑战性. 这项研究引入了FL的新型OE方法,可以有效地进行分布外检测,同时保持隐私和协作性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 异常暴露 (OE) 是神经网络中分布外 (OOD) 检测的实用方法.
- 传统的OE方法需要集中数据访问,由于客户端资源限制和数据隐私问题,限制了它们在联合学习 (FL) 中的使用.
研究的目的:
- 为FL提出一个现实的OE场景,其中中央服务器持有异常值数据,客户端持有分布式 (ID) 数据.
- 开发一种有效的基于OE的OOD检测方法,适合这种FL场景,平衡性能与隐私保护.
主要方法:
- 在佛罗里达州引入了一个新的OE场景,采用集中的异常数据和分散的ID数据.
- 开发了用于OOD检测的"内部分离和后台协作"方法.
- 实施了内部层特征向量的联合培训,这些向量与全球模型的后层进行了隐私保护的异常处理.
主要成果:
- 提出的方法有效地利用来自中央服务器的辅助异常值样本.
- 在开发的FL场景中,与基线方法相比,实现了显著的OOD检测性能.
- 保持隐私保护和协作培训绩效,是FL的核心目标.
结论:
- "内部分离和后台协作"方法为基于OE的OOD检测在FL提供了实用和有效的解决方案.
- 这种方法成功地解决了分散环境中的传统OE方法的局限性.
- 该研究表明,在不影响隐私或协作效率的情况下,在FL框架内增强OOD检测的可行性.
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