了解集中式和分散式联合式学习之间的普遍化和稳定性差距
IEEE transactions on pattern analysis and machine intelligence
|December 24, 2025
概括
集中式联合学习 (CFL) 显示出比分散式FL (DFL) 更高的概括性. 部分参与增强了CFL,而DFL需要特定的网络拓,以防止大规模培训中的性能崩.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 包含集中式 (CFL) 和分散式 (DFL) 框架,在实践中得到广泛应用.
- 目前的研究缺乏对CFL和DFL性能进行明确的比较,特别是在概括和稳定性方面.
- 随着通信减少,DFL在趋同方面表现有希望,但在经验上往往表现不佳.
研究的目的:
- 综合比较CFL和DFL的效率和泛化能力.
- 为选择合适的FL框架提供理论和经验证据.
- 确定影响CFL和DFL性能的关键因素.
主要方法:
- 稳定性和对平滑非凸的目标的概括性的理论分析.
- 对于CFL和DFL的概括界限的数学证明.
- 在常见的FL设置和场景中进行了广泛的实验验证.
主要成果:
- CFL总是比DFL更好地概括.
- 与完全参与相比,在CFL的部分参与产生了最佳的表现.
- DFL需要特定的网络拓来保持性能,随着培训规模的增加.
结论:
- 在联合学习中,CFL提供了卓越的概括性能.
- 在FL中选择框架时,应考虑参与策略和网络拓.
- 这些发现为优化联合学习部署提供了实际指导.
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