Towards a Patterns Driven Cloud Edge Continuum Architecture for eHealth

Nowadays different software applications rely on server cloud to host their resources and provide their services. The advent of cloud computing has revolutionized software development and maintenance processes but it also introduced vulnerabilities due to network connectivity and data storage, especially in real-time scenarios and sensitive sectors such as healthcare. These concerns have raised the necessity of a new kind of system capable of providing a solution to these problem, leading to the development of edge-based systems. Such systems aim to pre-process data near its source, optimizing bandwidth usage, ensuring data privacy and enabling timely data processing. This paper presents a Cloud Edge Architecture Patterns-based system for a Real-Time Drug Response Monitoring. The system architecture captures biometric data via IoT devices, processes it locally, and uses Federated Learning for global insights, exploiting Federated Patterns for model improvements. This approach not only enhances data privacy but also provides pharmaceutical insights and paves the way for advancements in precision medicine.