倡导沉默者:增强非参与客户的联合泛化
概括
联合学习 (FL) 努力处理多样化的客户数据 (非IID挑战). 本研究介绍了信息理论框架和新策略,以改善所有客户的模型概括性,即使是那些不参加培训的客户.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 分布式系统 分布式系统
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.
- 跨客户端的非IID数据分布对FL概括提出了重大挑战.
- 评估模型通用性是复杂的非参与的客户在FL.
研究的目的:
- 解决FL中参与和不参与的客户之间的被忽视的概括差距.
- 开发一个信息理论框架来量化FL泛化错误.
- 提出新的方法来增强FL模型对多样化和非参与客户数据的概括.
主要方法:
- 开发了FL的信息理论概括框架.
- 使用信息和分布差异的量化概括错误.
- 引入了加权聚合方法和两个客户选择策略.
主要成果:
- 拟议的框架为FL提供了理论概括界限.
- 权重聚合和客户选择策略提高了概括性能.
- 经验评估验证了拟议方法的有效性.
结论:
- 新的框架和战略有效地提高了FL的概括性,特别是对于不参与的客户.
- 解决数据分布差异是强大的FL的关键.
- 这些方法与理论预测保持一致,证明了实际效用.
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