StoCFL:用于非IID数据的随机聚类联合学习框架,具有动态的客户参与
Dun Zeng1, Xiangjing Hu2, Shiyu Liu1
1University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China.
概括
通过引入跨集群信息共享来解决非IID数据挑战,StoCFL增强了集群联合学习 (CFL). 这种新的框架可以提高分散系统中的模型性能和数据效率.
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) 系统由于分散和非独立同样分布 (非IID) 数据而面临性能下降.
- 现有的集群联合学习 (CFL) 方法缺乏跨集群通信,导致效率低下和模型性能差.
- 目前的CFL方法对准确的,预定义的客户端集群敏感,这往往是不切实际的.
研究的目的:
- 提出StoCFL,一种新的集群联合学习框架,旨在减轻非IID数据的影响.
- 开发一个灵活的CFL框架,以适应不同的客户参与和新客户增加.
- 在分散的学习环境中提高数据效率和模型性能.
主要方法:
- 斯托克FL实施了一个灵活的集群联合学习框架,具有跨集群信息共享机制.
- 该框架支持任意客户参与率,并在动态FL系统中容纳新加入的客户.
- 实验使用四个非IID设置和一个现实世界的数据集来评估性能.
主要成果:
- 斯托克FL表现出有希望的客户群集结果,即使群集的数量没有预先定义.
- 使用StoCFL训练的模型在各种非IID场景中显著优于基线方法.
- 该框架在模型性能和数据效率方面取得了实质性的改进.
结论:
- 通过其新的集群方法,StoCFL有效地解决了联合学习中的通用非IID问题.
- 拟议的框架为现实世界,动态的联合学习系统提供了灵活性和稳定性.
- StoCFL代表了在提高集群联合学习的性能和效率方面取得的重大进展.
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