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

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Abisek Dahal , Soumen Moulik , Rohan Mukherjee
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引用次数: 0

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.
FeL-MAR:物联网智能家居中基于联合学习的多住户活动识别
本研究探索并提出了一种基于联合学习(FL)的方法,利用从物联网(IoT)传感器收集的各种数据识别智能家居中的多住户活动。联邦学习模型在确保用户数据的最大隐私性、促进分散学习环境以及允许居民个人保留对其敏感信息的控制方面发挥着关键作用。本文的主要目标是准确识别和解释个人活动,允许他们对自己的机密信息保持主权。这将有助于在智能家居中提供丰富辅助生活体验的服务。所提出的系统设计具有很强的适应性,可以从多住宅行为中学习,预测并智能响应居民的需求和偏好,促进和谐、可持续的生活环境,同时维护从传感器收集到的数据的隐私性、保密性和控制性。所提出的 FeL-MAR 模型在多住户智能家居内的活动识别方面表现出色,其准确性和精确性优于其他模型,同时还能维护用户隐私。它建议有效利用 FL 和物联网传感器,这标志着智能家居技术取得了重大进展,在提高效率和用户体验的同时又不影响数据安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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