评估联合学习模拟器:对水平和垂直方法的比较分析
Ismail M Elshair1, Tariq Jamil Saifullah Khanzada1,2, Muhammad Farrukh Shahid3
1Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
联合学习 (FL) 通过在本地训练模型来实现保护隐私的机器学习. 本研究评估了FL框架,为组合横向和垂直方法在性能和沟通方面找到权衡.
科学领域:
- 机器学习 机器学习
- 分散式系统 分散式系统 分散式系统
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合学习 (FL) 在本地设备上训练模型,避免集中数据收集以提高隐私和安全性.
- 为了可扩展性和稳定性,FL依赖于设备之间的有效通信和协作.
- 现有的FL框架需要对不同的拓和方法进行评估.
研究的目的:
- 在联合学习中探索和评估各种分散和集中拓.
- 为了比较四个主要的端到端FL框架的性能:FedML,Flower,Flute和PySyft.
- 研究横向和垂直FL的组合,以应对同步和通讯空头等常见挑战.
主要方法:
- 专注于横向和垂直的联合学习系统.
- 使用了由FedAvg算法汇总的后勤回归模型.
- 在MNIST和时尚-MNIST图像数据集上进行实验,以评估框架的效率和性能.
主要成果:
- 评估了FedML,Flower,Flute和PySyft框架的效率和性能.
- 在不同的联合学习模拟框架中确定了性能上的权衡.
- 提供了关于将水平和垂直FL战略结合在一起的初步发现.
结论:
- 联合学习为集中式机器学习提供了一种保护隐私的替代方案.
- 框架选择涉及性能和沟通的权衡,特别是在结合FL方法时.
- 进一步的研究可以优化FL策略,用于实际的大规模部署.
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