基于对比编码器预训练的集群联合学习,用于异质数据
Ye Lin Tun1, Minh N H Nguyen2, Chu Myaet Thwal1
1Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, Gyeonggi-do 17104, South Korea.
概括
联合学习 (FL) 面临着数据异质性的挑战. 本研究介绍了基于预培训的对比的集群联合学习 (CP-CFL),通过利用未标记的数据进行预培训来提高模型的融合和性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 联合学习 (FL) 能够在保护数据隐私的同时实现协作模式培训.
- 在FL中的数据异质性显著降低了模型性能.
- 集群联合学习 (CFL) 旨在为客户群组创建个性化的模型.
研究的目的:
- 解决由于缺乏预先训练的模型而导致的CFL集群失败问题.
- 提高FL系统在异质环境中的性能和融合.
- 提出一种新的方法,比较基于预培训的集群联合学习 (CP-CFL).
主要方法:
- 使用自我监督的对比学习来预训练FL系统使用未标记的数据.
- 实施基于本地模型选择的客户群策略.
- 将自我监督的预培训与客户集群结合起来,形成CP-CFL.
主要成果:
- CP-CFL有效地解决了FL的数据异质性问题.
- 拟议的方法证明了改进的模型融合.
- 在异质的FL环境中进行了广泛的实验,验证了CP-CFL的有效性.
结论:
- 自主监督的预培训对于在佛罗里达州有效的客户集群至关重要.
- 在数据异质性下,CP-CFL为改善FL性能提供了强大的解决方案.
- 该研究强调了在分布式学习环境中利用未标记数据的潜力.
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