多中心层次联合学习与故障容忍机制,适应性边缘计算网络的多中心层次联合学习.
IEEE transactions on neural networks and learning systems
|March 28, 2024
概括
多中心层次联合学习 (MCHFL) 框架通过在边缘分布聚合中心来提高稳定性. 这种方法即使在50%的设备故障的情况下也保持了高精度,超过了传统的单中心联合学习模型.
科学领域:
- 人工智能的人工智能
- 分布式系统 分布式系统
- 机器学习 机器学习
背景情况:
- 传统的联合学习 (FL) 在很大程度上依赖于中央服务器,造成通信瓶和安全漏洞的风险.
- 现有的层次式FL (HFL) 模型提供了一些去中心化,但缺乏完整的边缘中心聚合和完整的故障容忍.
- 边缘计算环境需要强大的FL解决方案,能够承受设备和服务器故障.
研究的目的:
- 引入一个新的多中心层次联合学习 (MCHFL) 框架,旨在增强容错和去中心化.
- 在边缘计算场景中解决单中心FL架构的局限性.
- 开发一个以边缘为中心的FL模型聚合策略,以提高稳定性.
主要方法:
- 提出了多中心HFL (MCHFL) 框架,用基于边缘的分布式全球聚合中心取代单个中央服务器.
- 使用MNIST,FashionMNIST和CIFAR-10数据集进行实验,以评估MCHFL的性能.
- 在高率 (高达50%) 下评估MCHFL的准确性和稳定性.
主要成果:
- 与传统的单中心模型相比,MCHFL表现出卓越的性能,在显著的设备故障下保持高精度.
- 最大精度降低仅限于2.60% (MNIST),5.12% (FashionMNIST) 和16.73% (CIFAR-10) 在50%的的情况下.
- 在广泛的实验验证中,MCHFL表现出更快的融合速度和更强的稳定性.
结论:
- MCHFL框架代表了边缘多中心FL的开创性范式,在容错性和性能方面提供了显著的改进.
- 该研究提供了第一个已知的边缘多中心FL框架,具有理论基础.
- MCHFL被认可为在边缘环境中提供强大和高效的联合学习的高效解决方案.
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