FeL-MAR: Federated learning based multi resident activity recognition in IoT enabled smart homes

Abstract

This study explores and proposes the use of a Federated Learning (FL) based approach for recognizing multi-resident activities in smart homes utilizing a diverse array of data collected from Internet of Things (IoT) sensors. FL model is pivotal in ensuring the utmost privacy of user data fostering decentralized learning environments and allowing individual residents to retain control over their sensitive information. The main objective of this paper is to accurately recognize and interpret individual activities by allowing them to maintain sovereignty over their confidential information. This will help to provide a services that enrich assisted living experiences within the smart homes. The proposed system is designed to be adaptable learning from the multi-residential behaviors to predict and respond intelligently to the residents needs and preferences promoting a harmonious and sustainable living environment while maintaining privacy, confidentiality and control over the data collected from sensors. The proposed FeL-MAR model demonstrates superior performance in activity recognition within multi-resident smart homes, outperforming other models with its high accuracy and precision while maintaining user privacy. It suggest an effective use of FL and IoT sensors marks a significant advancement in smart home technologies enhancing both efficiency and user experience without compromising data security.