FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks DOI Open Access
Rehan Khan, Umer Saeed, Insoo Koo

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(24), P. 4907 - 4907

Published: Dec. 12, 2024

The rapid growth of Internet Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range systems and services. Wireless sensor networks (WSNs), crucial this ecosystem, often deployed in diverse challenging environments, making them susceptible faults such as software bugs, communication breakdowns, hardware malfunctions. These issues can compromise data accuracy, stability, reliability, ultimately jeopardizing system security. While advanced fault detection methods WSNs leverage machine learning approach achieve high they typically rely centralized learning, face scalability privacy challenges, especially when transferring large volumes data. In our experimental setup, we employ decentralized using federated with long short-term memory (FedLSTM) for WSNs, thereby preserving client privacy. This study utilizes temperature enhanced synthetic simulate various common faults: bias, drift, spike, erratic, stuck, data-loss. We evaluate the performance FedLSTM against based precision, sensitivity, F1-score. Additionally, analyze impacts varying participation rates number local training epochs. comparative analysis established models like one-dimensional convolutional neural network multilayer perceptron demonstrate promising results maintaining while reducing overheads server load.

Language: Английский

A comparative analysis of federated learning strategies for short-term photovoltaic power forecasting DOI
Vasilis Michalakopoulos,

Leonidas Spyropoulos,

Loukas Ilias

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 4223 - 4235

Published: April 8, 2025

Language: Английский

Citations

0
FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks DOI Open Access
Rehan Khan, Umer Saeed, Insoo Koo

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(24), P. 4907 - 4907

Published: Dec. 12, 2024

The rapid growth of Internet Things (IoT) devices has significantly increased reliance on sensor-generated data, which are essential to a wide range systems and services. Wireless sensor networks (WSNs), crucial this ecosystem, often deployed in diverse challenging environments, making them susceptible faults such as software bugs, communication breakdowns, hardware malfunctions. These issues can compromise data accuracy, stability, reliability, ultimately jeopardizing system security. While advanced fault detection methods WSNs leverage machine learning approach achieve high they typically rely centralized learning, face scalability privacy challenges, especially when transferring large volumes data. In our experimental setup, we employ decentralized using federated with long short-term memory (FedLSTM) for WSNs, thereby preserving client privacy. This study utilizes temperature enhanced synthetic simulate various common faults: bias, drift, spike, erratic, stuck, data-loss. We evaluate the performance FedLSTM against based precision, sensitivity, F1-score. Additionally, analyze impacts varying participation rates number local training epochs. comparative analysis established models like one-dimensional convolutional neural network multilayer perceptron demonstrate promising results maintaining while reducing overheads server load.

Language: Английский

Citations

0