Enhancing IoT Healthcare with Federated Learning and Edge Computing

Federated Learning is a machine learning technique which trains models that is inside multiple edge devices and only sends model updates like gradients or weights instead of raw complete data to the central server. Edge Computing is a computing technique that processes data close to where it is generated, for example, in IoT devices instead of centralized cloud servers. Federated Learning with Edge Computing, also called as FLEC, allows decentralized patient data processing, helping in healthcare analytics. To observe temperature, heart rate, and SpO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> levels, which is the oxygen level in blood, this study applies FLEC using simulated healthcare data activating alerts for any defects. A contrast study with Fog Computing, Cloud Computing, and Data Compression confirms that FLEC significantly reduces response time, which is the time taken for a system to react. FLEC decreases delays and reduces dependency on cloud while maintaining model accuracy and reducing loss by spreading upgrades across edge devices.