为了通用化,稳定性,公平性而联合学习:一项调查和基准
概括
本调查提供了一个关于联合学习 (FL) 的全面概述,这是一个保护隐私的AI方法. 它回顾了关键的研究领域,如概括,稳定性和公平性,强调了挑战和未来的方向.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 允许在不共享原始数据的情况下进行协作模型培训.
- 随着FL日益普及,需要对其进步进行结构化的概述.
- 在FL的现实挑战包括概括性,稳定性和公平性.
研究的目的:
- 系统地审查联合学习研究的最新发展.
- 定义联合学习的研究历史和术语.
- 确定未解决的问题,并建议未来的研究机会.
主要方法:
- 关于联合学习方法的综合文献综述.
- 将研究分类为概括性,稳定性和公平性.
- 在标准数据集上对代表性方法进行比较.
主要成果:
- 详细概述已建立和新兴的FL方法和数据集.
- 关键FL算法的实证基准测试.
- 确定FL的关键挑战和研究缺口.
结论:
- 联合学习是一个快速发展的领域,有着重要的研究方向.
- 解决泛化,稳定性和公平性对于实际FL部署至关重要.
- 需要进一步的研究来克服现有的局限性,并释放FL的全部潜力.
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