Implementation of Federated Learning in Wireless Sensor Network for Distributed Anomaly Detection
Keywords:
Pembelajaran Terfederasi, Jaringan Sensor Nirkabel, Deteksi Anomali, Kecerdasan, AIoTAbstract
The development of Wireless Sensor Network (WSN) technology opens up great opportunities for the application of smart monitoring systems in various fields, but presents challenges in terms of resource limitations, data security, and communication efficiency. Centralized machine learning approaches are often inefficient because they require large data transfers and pose a risk to privacy. Federated Learning (FL) provides an innovative solution by enabling distributed model training without sharing raw data between nodes. This study aims to analyze the performance and efficiency of FL implementation in WSN for distributed anomaly detection, with a focus on improving accuracy, energy efficiency, and reducing communication load. The method used is quantitative simulation-based experimentation, where each node trains a Deep Autoencoder model locally and aggregates parameters using the Federated Averaging (FedAvg) algorithm. The evaluation was conducted on three node count scenarios (10, 25, 50) and variations in non-IID data distribution (0.2, 0.5, 0.8). The results show an increase in anomaly detection accuracy of 3–5%, energy efficiency of up to 30%, and faster model convergence of up to 35 iterations compared to centralized methods. In conclusion, Federated Learning effectively improves the efficiency, resilience, and security of WSNs and provides a foundation for the future development of Edge Intelligence and Artificial Intelligence of Things (AIoT).
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