在联合学习中可解释的人工智能方法:系统审查
Titus Tunduny1, Bernard Shibwabo1
1School of Computing & Engineering Sciences, Strathmore University, Nairobi, Kenya.
JMIR AI
|February 3, 2026
概括
联合学习 (FL) 中可解释的人工智能 (AI) 是一个不断增长的研究领域. 这项研究回顾了可解释的FL,发现了新方法的潜力,特别是在关键应用中.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 像ChatGPT和Bard这样的生成性AI系统已经增加了AI的可访问性.
- 了解机器学习 (ML) 模型操作对于信任至关重要.
- 可解释AI (XAI) 解决了理解AI模型输出的挑战.
- 联合学习 (FL) 通过分散的模型使隐私保护AI成为可能.
研究的目的:
- 在联合学习环境中评估可解释AI的发展.
- 在可解释的FL研究中识别关键贡献,FL类型,应用领域,模型和方法.
- 编目用于该领域研究采购的数据库.
主要方法:
- 在八个主要的电子数据库中进行了系统的文献搜索.
- 这些数据库包括科学网,Scopus,PubMed,ACM数字图书馆,IEEE Xplore,Mendeley,BASE和谷歌学者.
主要成果:
- 对可解释性FL的研究正在扩大,主要集中在欧洲和亚洲.
- 数据隐私和有限的培训数据是 FL 采用的主要驱动力.
- 横向FL是主要的FL方法.
- 在当前的研究中,事后可解释性技术受到青.
结论:
- 可解释FL的领域显示出开发新方法和改进现有方法的巨大潜力.
- 对于关键应用领域尤其需要改进.
- 进一步的研究可以促进更大的信任和在敏感领域采用人工智能.
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