FedEff:高效的联合学习,为异质客户提供最佳的本地时代
1Department of Information Science and Technology, CEG Campus, Anna University, Chennai, India. narmk27@gmail.com.
Scientific reports
|November 6, 2025
概括
通过优化每个客户端的本地时代,联邦学习 (FL) 的效率得到了提高. 这种新的方法减少了培训时间和客户等待,提高了在异质环境中的模型融合.
科学领域:
- 机器学习 机器学习
- 分布式系统 分布式系统
- 人工智能的人工智能
背景情况:
- 联合学习 (FL) 允许在没有数据集中的情况下进行协作培训.
- 客户之间系统和统计异质性往往会降低FL的效率.
- 增加局部时代可以提高效率,但风险模式分歧和更慢的趋同.
研究的目的:
- 分析当地时代和FL的模型分歧之间的权衡.
- 提出一个高效的FL算法 (FedEff),解决异质性问题.
- 减少客户等待时间和整体培训时间.
主要方法:
- 经验差异分析,以了解当地时代的权衡.
- 开发FedEff,这是一个服务器端时代选择机制.
- 使用估计的回合时间 (ERT) 根据客户端速度来确定每个客户端的最佳本地时代.
主要成果:
- 一致的本地更新减少了平均差异,促进了稳定的趋同.
- 费德埃夫在客户等待时间和培训时间方面实现了显著的减少.
- 在异质环境中,FedEff的表现优于FedAvg和随机时代选择.
结论:
- 在异质性下优化局部时代对于高效的FL至关重要.
- 费德埃夫有效地平衡了当地培训和全球模式的融合.
- 拟议的算法提高了联合学习的性能和效率.
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