FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments

Massive data collection in modern systems has paved the way for data-driven machine learning, a promising technique for creating reliable and robust statistical models. By combining the data into centralized storage to develop a reliable learning model, there are concerns with privacy, ownership, and strict rules. It is self-evident that the samples in the typical machine learning centralized server paradigm have vastly different probability distributions of data supplied by each user. As a result, the typical model needs to be personalized for critical medical applications, and the deployment needs an efficient mechanism that can adapt to varying user inputs. Due to the heterogeneous and dynamic nature of critical medical IoT applications in such Edge/Fog scenarios, the privacy of patients become a crucial problem. Federated Learning, the model trained on diversity helps in addressing these concerns when used. This paper proposes the integration of Federated Learning for distributed Edge–Fog–Cloud architecture in the IoT smart healthcare sector. This paper presents FedSDM, the Federated Learning-based Smart Decision Making framework for the ECG data in microservice-based IoT medical applications. This proposal makes use of the advantages of Edge/Fog computing for real-time critical applications. It deploys the Federated Learning model at the Edge, Fog, and Cloud layers for performance comparison. The parameters considered for performance evaluation are energy consumption, network usage, cost, execution time, and latency. The proposed method shows that Edge-based deployment outperforms Fog and Cloud in terms of energy consumption, network usage, cost, execution time, and latency (i.e.) 0.3%, 2%, 15%, 11%, and 3% when compared with Fog and 1.6%, 31%, 41%, 24 % and 85% against Cloud respectively.