Heterogeneous Sensor Data Acquisition and Federated Learning for Resource Constrained IoT Devices - A validation

• Published in: IEEE sensors journal Vol. 23; no. 15; p. 1
• Main Authors: Rudraraju, Srinivasa Raju, Suryadevara, N.K., Negi, Atul
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Computational modeling; Constraints; Data acquisition; Data processing; Edge computing; Face recognition; Federated learning; Fog computing; Home environment; Intelligent sensors; Internet of Things; Machine learning; Nodes; Regression models; Sensor data acquisition; Sensors; Servers; Smart buildings; Smart home environment; Smart houses; Training
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3287580

This paper focused on applying federated learning to process heterogeneous sensor data in a fog computing-enabled smart home environment. The fusion of sensor data from vision sensor, digital ambient sensor and passive infrared sensor is done in a distributed manner and the data processing using federated learning on Raspberry Pi edge nodes. The system uses edge nodes to train the respective machine learning models using federated learning without sending data to a centralized server. Instead, results from the training on multiple edge nodes are aggregated at the central node to produce a final machine-learning model. Each edge node deploys the aggregated model to recognize the subjects in the smart home environment and trigger an alert in case unknown subjects are identified. Linear regression model for temperature prediction and logistic regression model for humidity prediction using the ambient parameters are also trained in a federated way on the same edge nodes. The developed system uses the federated learning Flower framework for training the models. The obtained validation accuracy was 66% for the machine learning models that were built on the resource constraint server and edge clients through the federated learning. The developed system can be used in environments where communication bandwidth is limited, or data privacy concerns exist.