FedRAD:通过关系自适应蒸进行异质联合学习
Jianwu Tang1,2, Xuefeng Ding1,2, Dasha Hu1,2
1College of Computer Science, Sichuan University, Chengdu 610065, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
联邦学习 (FL) 在非IID数据方面扎. 通过使用关系知识蒸来保持全球知识,提高物联网应用中的融合速度和准确性,FedRAD改进了FL.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 是一种分布式机器学习方法,在物联网 (IoT) 中保护数据隐私.
- 跨物联网设备的非独立和相同分布 (非IID) 数据会降低FL的性能,导致收速度缓慢和精度降低.
- 现有的 FL 方法在 Non-IID 设置中难以缓解"客户端遗忘"问题.
研究的目的:
- 提出FedRAD,一种新的联合学习方法,以应对非IID数据挑战.
- 加强在FL的当地培训阶段保留全球知识.
- 提高物联网分布式机器学习中的融合速度和分类准确性.
主要方法:
- FedRAD采用关系知识蒸,从更高维的角度挖掘高质量的全球知识.
- 一个智的自适应权重模块 (EWAW) 动态平衡单样和关系知识蒸损失.
- 培训了当地模型,以更好地保留全球知识并防止遗忘.
主要成果:
- 在实验评估中,FedRAD在先进的FL方法中表现出优越的性能.
- 拟议的方法在趋同速度方面取得了显著的改善.
- 在CIFAR10和CIFAR100数据集中观察到更高的分类准确性.
结论:
- 在联邦学习中,FedRAD有效地解决了非IID数据问题.
- 该方法增强了全球知识保留,从而提高了FL模型的性能.
- 在各种物联网环境中,FedRAD为保护隐私的机器学习提供了一个有希望的解决方案.
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