Implementasi Federated Learning Pada Wireless Sensor Network Untuk Deteksi Anomali Terdistribusi
Kata Kunci:
Pembelajaran Terfederasi, Jaringan Sensor Nirkabel, Deteksi Anomali, Kecerdasan, AIoTAbstrak
Perkembangan teknologi Wireless Sensor Network (WSN) membuka peluang besar bagi penerapan sistem pemantauan cerdas di berbagai bidang, namun menghadirkan tantangan pada aspek keterbatasan sumber daya, keamanan data, dan efisiensi komunikasi. Pendekatan pembelajaran mesin terpusat sering kali tidak efisien karena membutuhkan transfer data besar dan berisiko terhadap privasi. Federated Learning (FL) menjadi solusi inovatif dengan memungkinkan pelatihan model secara terdistribusi tanpa berbagi data mentah antar node. Penelitian ini bertujuan menganalisis kinerja dan efisiensi penerapan FL pada WSN untuk mendeteksi anomali secara terdistribusi, dengan fokus pada peningkatan akurasi, efisiensi energi, dan pengurangan beban komunikasi. Metode yang digunakan adalah kuantitatif eksperimental berbasis simulasi, di mana setiap node melatih model Deep Autoencoder secara lokal dan melakukan agregasi parameter menggunakan algoritma Federated Averaging (FedAvg). Evaluasi dilakukan pada tiga skenario jumlah node (10, 25, 50) dan variasi distribusi data non-IID (0.2, 0.5, 0.8). Hasil menunjukkan peningkatan akurasi deteksi anomali sebesar 3–5%, efisiensi energi hingga 30%, serta konvergensi model yang lebih cepat hingga 35 iterasi dibandingkan metode terpusat. Kesimpulannya, Federated Learning efektif meningkatkan efisiensi, ketahanan, dan keamanan WSN, serta menjadi fondasi bagi pengembangan Edge Intelligence dan Artificial Intelligence of Things (AIoT) di masa depan.
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REFERENSI
[1] F. P. E. Putra, D. A. M. Putra, A. Firdaus, and A. Hamzah, “Analisis Kecepatan Dan Kinerja Jaringan 5G (generasi ke 5) Pada Wilayah Perkotaan,” INFORMATICS Educ. Prof. J. Informatics, vol. 8, no. 1, p. 47, 2023, doi: 10.51211/itbi.v8i1.2439.
[2] F. Prasetyo, E. Putra, M. Riski, M. S. Yahya, and M. H. Ramadhan, “Mengenal Teknologi Jaringan Nirkabel Terbaru Teknologi 5G,” J. Sistim Inf. dan Teknol., vol. 5, no. 2, pp. 167–174, 2023, [Online]. Available: https://jsisfotek.org/index.php
[3] F. P. E. Putra, K. Mufidah, R. M. Ilhamsyah, S. A. Efendy, and S. N. R. Barokah, “Tinjauan Performa RouterOS Mikrotik dalam Jaringan Internet: Analisis Kinerja dan Kelayakan,” 2024. doi: 10.47709/digitech.v3i2.3446.
[4] F. P. E. Putra, U. Ubaidi, R. N. Saputra, F. M. Haris, and S. N. R. Barokah, “Application of Internet of Things Technology in Monitoring Water Quality in Fishponds,” 2024. doi: 10.47709/brilliance.v4i1.4231.
[5] F. P. Eka Putra, M. N. Arifin, K. Zulfana Imam, E. Saputra, and Sofiyullah, “Pengembangan Sistem Informasi Laboratorium Terintegerasi Sistem Akademik Menggunakan Agile Scrum,” J. Inf. dan Teknol., pp. 109–119, 2023, doi: 10.37034/jidt.v5i2.367.
[6] N. Muhammad Akbar, F. Prasetyo Eka Putra, K. Zulfana Imam, and M. Umar Mansyur, “Analisis Kinerja dan Interopabilitas STB Sebagai Server Penilaian Akhir Tahun,” J. Inf. dan Teknol., pp. 91–96, 2023, doi: 10.37034/jidt.v5i2.365.
[7] F. P. Eka Putra, F. Muslim, N. Hasanah, Holipah, R. Paradina, and R. Alim, “Analisis Komparasi Protokol Websocket dan MQTT Dalam Proses Push Notification,” J. Sistim Inf. dan Teknol., pp. 63–72, 2024, doi: 10.60083/jsisfotek.v5i4.325.
[8] F. P. E. Putra, U. Ubaidi, A. Zulfikri, G. Arifin, and R. M. Ilhamsyah, “Analysis of Phishing Attack Trends, Impacts and Prevention Methods: Literature Study,” 2024, pdfs.semanticscholar.org. doi: 10.47709/brilliance.v4i1.4357.
[9] N. Haidar Hari, F. P. Eka Putra, U. Hasanah, S. R. Sutarsih, and Riyan, “Transformasi Jaringan Telekomunikasi dengan Teknologi 5G: Tantangan, Potensi, dan Implikasi,” J. Inf. dan Teknol., pp. 146–150, 2023, doi: 10.37034/jidt.v5i2.357.
[10] F. Prasetyo Eka Putra, Moh Riski, Riyan, Yayu Rahma Febriani, and Muhammad Umar Mansyur, “Optimization Of Web Based Academic Information System Design To Increase Efficiency In Junior High Schools,” J. Inf. dan Teknol., pp. 150–158, 2024, doi: 10.60083/jidt.v6i2.545.
[11] F. Rezazadeh, L. Zanzi, F. Devoti, H. Chergui, X. Costa-Perez, and C. Verikoukis, “On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration,” IEEE Trans. Veh. Technol., vol. 72, no. 3, pp. 3473–3487, 2023, doi: 10.1109/TVT.2022.3218158.
[12] V. D. Huszar, V. K. Adhikarla, I. Negyesi, and C. Krasznay, “Toward Fast and Accurate Violence Detection for Automated Video Surveillance Applications,” IEEE Access, vol. 11, pp. 18772–18793, 2023, doi: 10.1109/ACCESS.2023.3245521.
[13] J. Zhang, Y. Liu, Z. Li, and Y. Lu, “Forecast-Assisted Service Function Chain Dynamic Deployment for SDN/NFV-Enabled Cloud Management Systems,” IEEE Syst. J., vol. 17, no. 3, pp. 4371–4382, 2023, doi: 10.1109/JSYST.2023.3263865.
[14] A. A. Khan et al., “GAN-IoTVS: A Novel Internet of Multimedia Things-Enabled Video Streaming Compression Model Using GAN and Fuzzy Logic,” IEEE Sens. J., vol. 23, no. 23, pp. 29434–29441, 2023, doi: 10.1109/JSEN.2023.3316088.
[15] Z. Lai et al., “Merak: An Efficient Distributed DNN Training Framework with Automated 3D Parallelism for Giant Foundation Models,” IEEE Trans. Parallel Distrib. Syst., vol. 34, no. 5, pp. 1466–1478, 2023, doi: 10.1109/TPDS.2023.3247001.
[16] H. Xie, M. Xia, P. Wu, S. Wang, and K. Huang, “Decentralized Federated Learning With Asynchronous Parameter Sharing for Large-Scale IoT Networks,” IEEE Internet Things J., vol. 11, no. 21, pp. 34123–34139, 2024, doi: 10.1109/JIOT.2024.3354869.
[17] S. Tuli and N. K. Jha, “AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 42, no. 11, pp. 4038–4051, 2023, doi: 10.1109/TCAD.2023.3273992.
[18] J. Qiu et al., “Large AI Models in Health Informatics: Applications, Challenges, and the Future,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 12, pp. 6074–6087, 2023, doi: 10.1109/JBHI.2023.3316750.
[19] L. Zhou, S. Leng, Q. Wang, and Q. Liu, “Integrated Sensing and Communication in UAV Swarms for Cooperative Multiple Targets Tracking,” IEEE Trans. Mob. Comput., vol. 22, no. 11, pp. 6526–6542, 2023, doi: 10.1109/TMC.2022.3193499.
[20] J. Yao et al., “Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI,” IEEE Trans. Knowl. Data Eng., vol. 35, no. 7, pp. 6866–6886, 2023, doi: 10.1109/TKDE.2022.3178211.
[21] Z. Liu et al., “PPRU: A Privacy-Preserving Reputation Updating Scheme for Cloud-Assisted Vehicular Networks,” IEEE Trans. Veh. Technol., vol. 74, no. 2, pp. 1877–1892, 2023, doi: 10.1109/TVT.2023.3340723.
[22] C. Park, J. Lee, Y. Kim, J. G. Park, H. Kim, and D. Hong, “An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks,” IEEE Internet Things J., vol. 10, no. 3, pp. 2330–2345, 2023, doi: 10.1109/JIOT.2022.3211346.
[23] B. S. Lin, C. W. Peng, I. J. Lee, H. K. Hsu, and B. S. Lin, “System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 7, pp. 3549–3558, 2023, doi: 10.1109/JBHI.2023.3271463.
[24] A. Abouzarkhanifard, H. E. Chimeh, M. Al Janaideh, and L. Zhang, “FEM-Inclusive Transfer Learning for Bistable Piezoelectric MEMS Energy Harvester Design,” IEEE Sens. J., vol. 23, no. 4, pp. 3521–3531, 2023, doi: 10.1109/JSEN.2023.3235198.
[25] B. Mao, J. Liu, Y. Wu, and N. Kato, “Security and Privacy on 6G Network Edge: A Survey,” IEEE Commun. Surv. Tutorials, vol. 25, no. 2, pp. 1095–1127, 2023, doi: 10.1109/COMST.2023.3244674.
[26] J. Li et al., “Toward Optimal Real-Time Volumetric Video Streaming: A Rolling Optimization and Deep Reinforcement Learning Based Approach,” IEEE Trans. Circuits Syst. Video Technol., vol. 33, no. 12, pp. 7870–7883, 2023, doi: 10.1109/TCSVT.2023.3277893.
[27] A. Mahapatra, S. K. Majhi, K. Mishra, R. Pradhan, D. C. Rao, and S. K. Panda, “An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum,” IEEE Access, vol. 12, pp. 14334–14349, 2024, doi: 10.1109/ACCESS.2024.3357122.
[28] X. Nie et al., “Pro-Tuning: Unified Prompt Tuning for Vision Tasks,” IEEE Trans. Circuits Syst. Video Technol., vol. 34, no. 6, pp. 4653–4667, 2024, doi: 10.1109/TCSVT.2023.3327605.
[29] F. A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion Models in Vision: A Survey,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 9, pp. 10850–10869, 2023, doi: 10.1109/TPAMI.2023.3261988.
[30] R. Wang, J. Lai, Z. Zhang, X. Li, P. Vijayakumar, and M. Karuppiah, “Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing,” IEEE J. Biomed. Heal. Informatics, vol. 27, no. 2, pp. 854–865, 2023, doi: 10.1109/JBHI.2022.3157725.
[31] T. Fiori, F. G. Lavacca, F. Valente, and V. Eramo, “Proposal and Investigation of a Lite Time Sensitive Networking Solution for the Support of Real Time Services in Space Launcher Networks,” IEEE Access, vol. 12, pp. 10664–10680, 2024, doi: 10.1109/ACCESS.2024.3353466.
[32] Y. Cui et al., “Lightweight Neural Network With Knowledge Distillation for CSI Feedback,” IEEE Trans. Commun., vol. 72, no. 8, pp. 4917–4929, 2024, doi: 10.1109/TCOMM.2024.3377724.
[33] Y. Mo et al., “HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images,” IEEE Trans. Med. Imaging, vol. 42, no. 6, pp. 1696–1706, 2023, doi: 10.1109/TMI.2023.3236011.
[34] T. Li, W. Bai, Q. Liu, Y. Long, and C. L. P. Chen, “Distributed Fault-Tolerant Containment Control Protocols for the Discrete-Time Multiagent Systems via Reinforcement Learning Method,” IEEE Trans. Neural Networks Learn. Syst., vol. 34, no. 8, pp. 3979–3991, 2023, doi: 10.1109/TNNLS.2021.3121403.
[35] L. M. Halman and M. J. F. Alenazi, “MCAD: A Machine Learning Based Cyberattacks Detector in Software-Defined Networking (SDN) for Healthcare Systems,” IEEE Access, vol. 11, pp. 37052–37067, 2023, doi: 10.1109/ACCESS.2023.3266826.
[36] S. Long, Y. Zhang, Q. Deng, T. Pei, J. Ouyang, and Z. Xia, “An Efficient Task Offloading Approach Based on Multi-Objective Evolutionary Algorithm in Cloud-Edge Collaborative Environment,” IEEE Trans. Netw. Sci. Eng., vol. 10, no. 2, pp. 645–657, 2023, doi: 10.1109/TNSE.2022.3217085.
[37] S. Bergies, T. M. Aljohani, S. F. Su, and M. Elsisi, “An IoT-Based Deep-Learning Architecture to Secure Automated Electric Vehicles Against Cyberattacks and Data Loss,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 54, no. 9, pp. 5717–5732, 2024, doi: 10.1109/TSMC.2024.3409314.
[38] Z. Li et al., “Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 21, no. 4, pp. 835–845, 2024, doi: 10.1109/TCBB.2022.3184319.
[39] Y. Song, Q. Zheng, B. Liu, and X. Gao, “EEG Conformer: Convolutional Transformer for EEG Decoding and Visualization,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 710–719, 2023, doi: 10.1109/TNSRE.2022.3230250.
[40] S. Wu, N. Chen, A. Xiao, P. Zhang, C. Jiang, and W. Zhang, “AI-Empowered Virtual Network Embedding:A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, 2024, doi: 10.1109/COMST.2024.3424533.
[41] Q. Zhang, Y. Luo, H. Jiang, and K. Zhang, “Aerial Edge Computing: A Survey,” IEEE Internet Things J., vol. 10, no. 16, pp. 14357–14374, 2023, doi: 10.1109/JIOT.2023.3263360.
[42] A. Bessadok, M. A. Mahjoub, and I. Rekik, “Graph Neural Networks in Network Neuroscience,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 5, pp. 5833–5848, 2023, doi: 10.1109/TPAMI.2022.3209686.
[43] D. Das Sharma, “Novel Composable and Scaleout Architectures Using Compute Express Link,” IEEE Micro, vol. 43, no. 2, pp. 9–19, 2023, doi: 10.1109/MM.2023.3235972.
[44] Y. Ying, Q. Liu, M. Wu, and Y. Zhai, “Online Energy Management Strategy of the Flexible Smart Traction Power Supply System,” IEEE Trans. Transp. Electrif., vol. 9, no. 1, pp. 981–994, 2023, doi: 10.1109/TTE.2022.3192141.
[45] D. Rao, T. Xu, and X. J. Wu, “TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network,” IEEE Trans. Image Process., 2023, doi: 10.1109/TIP.2023.3273451.
[46] X. Wen et al., “PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-Step Point Moving Paths,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 1, pp. 852–867, 2023, doi: 10.1109/TPAMI.2022.3159003.
[47] X. Zhang, Z. Mao, N. Chimitt, and S. H. Chan, “Imaging Through the Atmosphere Using Turbulence Mitigation Transformer,” IEEE Trans. Comput. Imaging, vol. 10, pp. 115–128, 2024, doi: 10.1109/TCI.2024.3354421.
[48] B. Li et al., “Dense Nested Attention Network for Infrared Small Target Detection,” IEEE Trans. Image Process., vol. 32, pp. 1745–1758, 2023, doi: 10.1109/TIP.2022.3199107.
[49] H. Yuan et al., “Cost-Efficient Task Offloading in Mobile Edge Computing With Layered Unmanned Aerial Vehicles,” IEEE Internet Things J., vol. 11, no. 19, pp. 30496–30509, 2024, doi: 10.1109/JIOT.2024.3408216.
[50] S. Chu, C. Gao, M. Xu, K. Ye, Z. Xiao, and C. Xu, “Efficient Multi-Task Computation Offloading Game for Mobile Edge Computing,” IEEE Trans. Serv. Comput., vol. 17, no. 1, pp. 30–46, 2024, doi: 10.1109/TSC.2023.3332140.
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Hak Cipta (c) 2025 Panji Cahya Prasetyo, Wildan Ramadhani (Penulis)
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