Internet of Health Things (IoHT) have allowed connected health paradigm ubiquitous. 5 G supported healthcare vertical allows IoHT to offer connected health monitoring with quality of service and ultra-low latency. Deep learning has shown potential in processing massive amount of IoHT data that are generated daily, automate connected healthcare workflows, and help in decision making processes. However, three important challenges need to be addressed to attain long term healthcare-related sustainability – data security, data privacy, and social acceptance of deep learning process. In this paper, we propose a framework that will allow healthcare sustainability through the following contributions 1) ensure privacy of training dataset, 2) support the aggregation of the global model gradients through a private Blockchain-brokered entity, 3) support trustworthiness and provenance of the federated clients by blockchain and off-chain, 4) share the dataset, train the model and share trained model among the federated clients in an encrypted fashion, and 5) add explainability and reasoning of deep learning process to make the model acceptable by the society. We will present the detailed design of our proposed sustainable system, the implementation details and test results. The test results show promising prospect of achieving sustainability of IoHT-enabled connected health applications.

健康物联网 (IoHT) 使互联健康模式无处不在。5G 支持的垂直医疗保健允许 IoHT 提供具有服务质量和超低延迟的连接健康监测。深度学习在处理每天生成的大量 IoHT 数据、自动化连接的医疗保健工作流程以及帮助决策过程方面显示出潜力。然而，要实现与医疗保健相关的长期可持续性，需要解决三个重要挑战——数据安全、数据隐私和深度学习过程的社会接受度。在本文中，我们提出了一个框架，该框架将通过以下贡献实现医疗保健的可持续性：1）确保训练数据集的隐私，2）通过私有区块链代理实体支持全局模型梯度的聚合，3) 通过区块链和链下支持联合客户端的可信度和来源，4) 以加密方式在联合客户端之间共享数据集、训练模型和共享训练模型，以及 5) 添加深度学习过程的可解释性和推理使模型为社会所接受。我们将介绍我们提议的可持续系统的详细设计、实施细节和测试结果。测试结果显示，实现支持 IoHT 的互联健康应用的可持续性前景广阔。我们将介绍我们提议的可持续系统的详细设计、实施细节和测试结果。测试结果显示，实现支持 IoHT 的互联健康应用的可持续性前景广阔。我们将介绍我们提议的可持续系统的详细设计、实施细节和测试结果。测试结果显示，实现支持 IoHT 的互联健康应用的可持续性前景广阔。