跨客户协调员在联合学习框架中,以克服异质性.
IEEE transactions on neural networks and learning systems
|August 23, 2024
概括
使用异质数据进行联合学习是具有挑战性的. 我们的 FedUCS 框架使用统一的编码空间和跨客户端协调员来确保一致的学习目标,提高模型性能.
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) 通过在当地培训模型来保护数据隐私.
- 跨客户端的数据异质性导致不同的学习目标,这是一个关键的FL挑战.
- 现有的FL方法与非IID (独立和相同分布) 数据扎.
研究的目的:
- 提出一个联合学习框架,FedUCS,在异质数据设置中解决不同的学习目标.
- 为客户提供统一的编码规则,以实现一致的模型培训.
- 在现实世界,多样化的数据环境中增强保护隐私的机器学习.
主要方法:
- 开发了FedUCS,这是一个使用统一编码空间的联合学习框架.
- 引入了一个跨客户端协调员来监督统一的客户端编码.
- 实现了一种用于知识保留的部分记忆机制.
- 应用监督对比学习来提高编码空间的区分能力.
主要成果:
- FedUCS有效地减轻了因异质数据引起的目标差异.
- 统一的编码空间确保了跨客户端的一致学习.
- 实验结果验证了框架在非IID设置中的卓越性能.
- 部分记忆和对比学习组件增强了模型的稳定性和准确性.
结论:
- FedUCS提供了一个强大的解决方案,用于使用异质数据进行联合学习.
- 拟议的跨客户端统一编码空间是FL的重大进步.
- 这一框架增强了保护隐私的机器学习的实用性.
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