意识到移动性的联合学习,考虑多个网络
Daniel Macedo1, Danilo Santos2, Angelo Perkusich2
1Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58429-900, Paraiba, Brazil.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
联合学习 (FL) 培训效率由MoFeL改进,这是一个解决流动性问题的新算法. 在移动场景中,MoFeL可将培训周期提高156.5%.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 允许分布式机器学习 (ML) 模型训练,同时保留用户数据的所有权.
- 用户移动性和网络断开可以显著破坏FL培训效率,导致客户学.
研究的目的:
- 提出MoFeL,一个新的FL协调算法,旨在在用户移动的情况下保持高效的培训性能.
- 评估MoFeL在处理多个网络和不同的中央服务器配置方面的有效性.
主要方法:
- 开发并模拟了用于联合学习的MoFeL协调算法.
- 利用图像分类应用程序与卷积神经网络进行实验评估.
- 在高流动性场景中比较了MoFeL与传统FL协调算法的性能.
主要成果:
- 在高流动性条件下,MoFeL在FL培训协调方面表现优越.
- 与非移动意识算法相比,拟议的算法实现了156.5%的培训周期.
- 在不同中央服务器的多个网络中,MoFeL有效地管理了培训.
结论:
- 在动态,移动环境中,MoFeL显著提高了联合学习的效率和稳定性.
- 该算法提供了一个切实可行的解决方案,用于在现实世界FL部署中克服客户端退出挑战.
- MoFeL代表了分布式机器学习协调策略的重大进步.
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