###### 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. federated learning; K-Anonymity; privacy-preserving; hierarchical clustering +
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The effective utilization of personal healthcare data are a critical requirement. Conventional machine-learning distributed algorithms are trained on existing datasets to track health information [12; 13; 14]. Three significant research challenges in modern healthcare systems need to be considered. First, real-time data are more critical and difficult to access because of the different privacy policies of isolated organizations. Secure and open access to these isolated data hubs is challenging when managing a central global learning model [15]. A distributed federated learning (FL) framework was designed to optimize the learning model, and has shown promising results [16]. Second, personalization is the most crucial issue. Different clients of smart healthcare systems use multiple devices with varying processing capacities [17]. Therefore, standard server-based learning models are unsuitable for maintaining personalization, as FL distributes the learning model among all the connected clients and provides flexibility to adjust the learning model according to the client's resources. However, personalization requires further investigation to enhance the performance of the distributed learning techniques. Third, all network nodes can never directly connect to the global learning model because of the network range and other communication constraints, which is not the case in FL because the global model is transmitted only to the clients participating in the network [18]. However, it is practically difficult for a global model to maintain direct connectivity with the client models [19]. A major problem in FL is scalability, as it distributes the learning model over all the devices and aggregates all the updates from the client models [20]. Currently, the advancement in positioning technology has led to the introduction of location-based service (LBS) technologies such as GPS, Baidu Map, and other wireless positioning technologies in intelligent devices, which facilitate the network administrator to create distance-based clusters of the participating devices [21]. These LBS technologies also compromise the location and pose a severe risk to clients with healthcare ratings. Location privacy, along with data privacy, is a critical challenge, particularly in the case of distributed FL [22]. Therefore, it is essential to design a solution that can hide the location to secure the personal information of clients.