Real-Time Environmental Monitoring in Smart Buildings Using Federated Learning

Nowadays, modern office environments require the maintenance of optimal working conditions to enhance employee productivity and well-being. Traditional centralized data processing systems present challenges such as data privacy concerns, high communication latency, and inefficient use of network resources. Addressing these challenges is essential for improving office space efficiency and comfort. FL offers an innovative solution, by decentralizing data processing, allowing it to occur locally on edge devices. This approach provides several advantages over traditional centralized methods, including enhanced data privacy, reduced communication latency, and more efficient use of network resources. The proposed solution involves implementing FL models in Internet of Things (IoT) device systems within office buildings. Key environmental parameters measured by sensors include illuminance, air quality, temperature, and noise levels. These measurements are crucial for optimizing the office environment, thus improving operational efficiency and employee satisfaction.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.07.081
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

Real-Time Environmental Monitoring in Smart Buildings Using Federated Learning

Ricardo Jardim‐Gonçalves, Paula Ventura, Paulo Figueiras, Rúben Costa et al.
Procedia Computer Science
Privacy-Preserving Technologies in Data
article

Real-Time Environmental Monitoring in Smart Buildings Using Federated Learning

Ricardo Jardim‐Gonçalves, Paula Ventura, Paulo Figueiras, Rúben Costa, Mohammad Khodamoradi
article en

Abstract

Nowadays, modern office environments require the maintenance of optimal working conditions to enhance employee productivity and well-being. Traditional centralized data processing systems present challenges such as data privacy concerns, high communication latency, and inefficient use of network resources. Addressing these challenges is essential for improving office space efficiency and comfort. FL offers an innovative solution, by decentralizing data processing, allowing it to occur locally on edge devices. This approach provides several advantages over traditional centralized methods, including enhanced data privacy, reduced communication latency, and more efficient use of network resources. The proposed solution involves implementing FL models in Internet of Things (IoT) device systems within office buildings. Key environmental parameters measured by sensors include illuminance, air quality, temperature, and noise levels. These measurements are crucial for optimizing the office environment, thus improving operational efficiency and employee satisfaction.

Procedia Computer ScienceVol. 263
Uninova (PT), Universidade Nova de Lisboa (PT)
Instituto de Desenvolvimento de Novas Tecnologias
Climate action
Openalex Percentile: Top 89%
Privacy-Preserving Technologies in Data
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