Blockchain technology combined with Federated Learning (FL) offers a promising solution for enhancing privacy, security, and efficiency in medical IoT applications across edge, fog, and cloud computing environments. This approach enables multiple medical IoT devices at the network edge to collaboratively train a global machine learning model without sharing raw data, addressing privacy concerns associated with centralized data storage. This paper presents a blockchain and FL-based Smart Decision Making framework for ECG data in microservice-based IoT medical applications. Leveraging edge/fog computing for real-time critical applications, the framework implements a FL model across edge, fog, and cloud layers. Evaluation criteria including energy consumption, latency, execution time, cost, and network usage show that edge-based deployment outperforms fog and cloud, with significant advantages in energy consumption (0.1% vs. Fog, 0.9% vs. Cloud), network usage (1.1% vs. Fog, 31% vs. Cloud), cost (3% vs. Fog, 20% vs. Cloud), execution time (16% vs. Fog, 28% vs. Cloud), and latency (1% vs. Fog, 79% vs. Cloud).

中文翻译：

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在 Edge-Fog-Cloud 计算环境中利用区块链和联合学习，使用 IoT 中的 ECG 数据进行智能决策


区块链技术与联邦学习 （FL） 相结合，为跨边缘、雾和云计算环境增强医疗 IoT 应用的隐私性、安全性和效率提供了一种有前途的解决方案。这种方法使网络边缘的多个医疗 IoT 设备能够在不共享原始数据的情况下协作训练全球机器学习模型，从而解决与集中数据存储相关的隐私问题。本文提出了一个基于区块链和 FL 的智能决策框架，用于基于微服务的 IoT 医疗应用中的 ECG 数据。该框架将边缘/雾计算用于实时关键应用程序，跨边缘、雾和云层实施 FL 模型。包括能耗、延迟、执行时间、成本和网络使用情况在内的评估标准表明，基于边缘的部署优于雾和云，在能耗（0.1% vs. Fog，0.9% vs. Cloud）、网络使用率（1.1% vs. Fog，31% vs. Cloud）、成本（3% vs. Fog，20% vs. Cloud）、执行时间（16% vs. Fog，28% vs. Cloud）和延迟（1% vs. Fog， 79% vs. Cloud）。