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Lightweight Federated Learning Approach for Resource-Constrained Internet of Things
1Department of Computer Engineering, College of Information Technology, University of Bahrain, Sakhair P.O. Box 32038, Bahrain.
Sensors (Basel, Switzerland)
|September 27, 2025
Summary
This study introduces a new federated learning method for the Internet of Things (IoT) that uses k-nearest neighbors (k-NN) and majority voting. It significantly cuts communication needs and boosts energy efficiency for longer network life.
Area of Science:
- Computer Science
- Artificial Intelligence
- Internet of Things
Background:
- Federated learning (FL) is key for distributed intelligence in resource-constrained Internet of Things (IoT) environments.
- Traditional FL struggles with high processing, memory, and communication demands, reducing battery life in IoT devices.
- This limits the operational longevity and practicality of FL in battery-powered IoT networks.
Purpose of the Study:
- To propose a streamlined, single-shot federated learning approach for resource-constrained IoT.
- To minimize communication overhead and enhance energy efficiency.
- To extend the operational longevity of IoT networks through improved FL.
Main Methods:
- A novel single-shot federated learning approach is presented.
- The k-nearest neighbors (k-NN) algorithm is used for edge-level pattern recognition.
- Majority voting at the server/base station achieves global pattern recognition consensus.
Main Results:
- The proposed approach significantly reduces communication rounds compared to traditional FL.
- It maintains competitive classification accuracy.
- Energy efficiency is enhanced, leading to extended network lifetime.
Conclusions:
- The single-shot federated learning method is effective for resource-constrained IoT environments.
- It addresses the limitations of traditional FL by reducing communication and improving energy efficiency.
- This approach offers a practical solution for deploying intelligent IoT networks with extended operational capabilities.
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