有效的分割混合联合学习,用于按需和现场定制
Junyuan Hong1, Haotao Wang2, Zhangyang Wang2
1Department of Computer Science and Engineering, Michigan State University.
概括
联邦学习 (FL) 参与者现在可以根据需求定制模型大小和稳定性. 这种Split-Mix FL策略有效地适应异质资源,增强实际应用.
科学领域:
- 人工智能的人工智能
- 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) 允许在不需要共享原始数据的情况下进行协作模式培训.
- 参与者资源异质性 (硬件,推断速度) 挑战了现有的FL方法.
- 需要适应性模型,满足各种推断要求.
研究的目的:
- 为异质参与者提出一个新的Spli-Mix FL战略.
- 为了使模型尺寸和强度在培训后的需求定制.
- 提高FL在动态环境中的效率和适用性.
主要方法:
- 学习一组不同大小和稳定性级别的基础子网络.
- 根据特定推断需求量身定制的子网络的按需聚合.
- 在异质资源环境中实施Split-Mix FL战略.
主要成果:
- 拟议的Split-Mix FL战略实现了有效的现场定制.
- 与现有的异质架构FL方法相比,证明了更高的性能.
- 在通信,存储和推理方面验证了高效率.
结论:
- 在实际的飞行场景中,Split-Mix FL有效地解决了异质性和动态.
- 允许灵活和高效的模型适应各种推断要求.
- 显著扩大了联合学习的适用性.
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