FedMEKT:基于蒸的嵌入式知识转移,用于多式联网学习的多式联网学习
Huy Q Le1, Minh N H Nguyen2, Chu Myaet Thwal1
1Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, 17104, Republic of Korea.
概括
联合学习 (FL) 现在支持使用FedMEKT的多式联网数据,这是一个新的半监督框架. 这种方法提高了模型性能和隐私,同时降低了通信成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 联合学习 (FL) 传统上侧重于单模式数据.
- 现有的FL系统通常需要标记客户端数据,从而限制了现实世界的适用性.
- 在 FL 中利用多式联运数据对于个性化的应用程序至关重要.
研究的目的:
- 引入FedMEKT,一个新的多式联网学习框架.
- 为了应对FL的模式差异和有限的标签数据的挑战.
- 为了利用半监督式学习来实现多模式数据表示.
主要方法:
- 开发了FedMEKT框架,包括本地多式联网自动编码器学习,通用多式联网自动编码器构建和通用分类器学习.
- 实现了基于蒸的多式联运嵌入知识传输机制,用于服务器-客户端数据交换.
- 利用上游和下游多式联通嵌入知识传输,用于代的全球编码器更新.
主要成果:
- 在四个多模式数据集的线性评估中,FedMEKT展示了卓越的全球编码器性能.
- 该框架确保了个人数据和模型参数的用户隐私.
- 与现有的基线方法相比,实现了较低的通信成本.
结论:
- 通过使用半监督方法,FedMEKT有效地实现了多式联网学习.
- 拟议的框架克服了单模FL和标记数据依赖的局限性.
- 在分散的环境中,FedMEKT为多式联运数据分析提供了一种保护隐私的高效解决方案.
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