对于集群联合学习的活跃客户端选择.
概括
积极学习通过选择信息型客户端来增强集群联合学习 (CFL),加快培训,在数据异质性下提高模型准确性. 这种方法在分布式机器学习中减少了客户参与和通信开销.
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) 是一种具有隐私和通信限制的分布式机器学习框架.
- 集群FL (CFL) 通过学习客户群的定制模型来解决数据异质性.
- 现有的CFL方法由于客户选择效率低下,特别是非IID数据,因此存在缓慢的融合和有限的性能问题.
研究的目的:
- 为集群联合学习 (ACFL) 引入一种新的主动客户选择策略.
- 通过选择最具信息性的客户端进行模型更新来提高CFL的效率和准确性.
主要方法:
- 拟议的ACFL方法使用主动学习 (AL) 度量 (例如,不确定性抽样,QBC,损失) 来在每个集群中选择客户.
- 在每一轮中,集群都会从一小部分信息客户中识别和汇总更新.
- 在类不平衡条件下对MNIST,CIFAR-10和LEAF数据集的实证评估.
主要成果:
- 与基线FL和CFL方法相比,ACFL显著加快了学习过程.
- 该方法需要较少的客户参与,同时实现更好的模型准确性.
- 在ACFL中,通信开支显著减少.
结论:
- 积极的客户端选择是改善集群联合学习的有效策略.
- 在异质环境中,ACFL为高效准确的分布式机器学习提供了一个有前途的解决方案.
- 拟议的方法通过智能地根据信息性选择客户来提高CFL的性能.
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