SHFL: K-Anonymity-Based Secure Hierarchical Federated Learning Framework for Smart Healthcare Systems

Abstract

Dynamic and smart Internet of Things (IoT) infrastructures allow the development of smart healthcare systems, which are equipped with mobile health and embedded healthcare sensors to enable a broad range of healthcare applications. These IoT applications provide access to the clients’ health information. However, the rapid increase in the number of mobile devices and social networks has generated concerns regarding the secure sharing of a client’s location. In this regard, federated learning (FL) is an emerging paradigm of decentralized machine learning that guarantees the training of a shared global model without compromising the data privacy of the client. To this end, we propose a K-anonymity-based secure hierarchical federated learning (SHFL) framework for smart healthcare systems. In the proposed hierarchical FL approach, a centralized server communicates hierarchically with multiple directly and indirectly connected devices. In particular, the proposed SHFL formulates the hierarchical clusters of location-based services to achieve distributed FL. In addition, the proposed SHFL utilizes the K-anonymity method to hide the location of the cluster devices. Finally, we evaluated the performance of the proposed SHFL by configuring different hierarchical networks with multiple model architectures and datasets. The experiments validated that the proposed SHFL provides adequate generalization to enable network scalability of accurate healthcare systems without compromising the data and location privacy.

Keywords

• federated learning
• K-Anonymity
• privacy-preserving
• hierarchical clustering