FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring
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DOI：
10.1109/tmc.2020.3045266
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发表时间：
2020-12
影响因子：
7.9
通讯作者：
Qiong Wu;Xu Chen;Zhi Zhou;Junshan Zhang
中科院分区：
文献类型：
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作者：
Qiong Wu;Xu Chen;Zhi Zhou;Junshan Zhang
居家健康监测在全球范围内引起了老龄化人口的高度关注。随着物联网(IoT)设备访问的丰富用户健康数据和机器学习的最新发展，智能医疗已经看到了许多成功的故事。然而，现有的家庭健康监测方法还没有对用户数据隐私给予足够的重视，因此还远远没有准备好大规模的实际部署。本文提出了一种基于云端的家庭健康监测联邦学习框架FedHome，该框架从网络边缘的多个家庭学习云中共享的全局模型，通过将用户数据保存在本地来实现数据隐私保护。针对用户监测数据固有的不平衡和非IID分布的特点，设计了一种产生式卷积自动编码器(GCAE)，其目的是通过从用户个人数据生成类平衡数据集来改进模型，从而实现准确和个性化的健康监测。此外，GCAE在云和边之间的传输是轻量级的，这有助于降低联邦学习在FedHome中的通信成本。基于真实人类活动识别数据的广泛实验证实，FedHome的性能大大优于现有的广泛采用的方法。
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user’s monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user’s personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.