Tantangan dan Tren Masa Depan Keamanan Federated Learning pada Ekosistem IoT Tinjau Pustaka Sistematis

Ardiansyah Hasan; Fikri Farhan

doi:10.33365/jtikom.v7i1.1953

Ardiansyah Hasan Universitas Majalengka, Jawa Barat
Fikri Farhan Universitas Majalengka, Jawa barat

DOI: https://doi.org/10.33365/jtikom.v7i1.1953

Keywords: Federated Learning, Internet of Things, Security, Systematic Literature Review, Blockchain

Abstract

The rapid growth of the Internet of Things (IoT) ecosystem has generated a massive surge in data, driving the utilization of Federated Learning (FL) as a decentralized model training solution that guarantees user privacy. Despite its promise, the implementation of FL in IoT infrastructures is frequently hindered by cybersecurity vulnerabilities and hardware computational constraints. Therefore, this paper presents a Systematic Literature Review (SLR) guided by the PRISMA 2020 standards to analyze the architectures, security protocols, and future projections of FL in IoT environments. Through the extraction and synthesis of 25 selected primary literatures indexed in the Scopus database, the findings indicate that Deep Learning models-particularly CNN and LSTM-are the most dominant approaches. Furthermore, the combination of Homomorphic Encryption and Differential Privacy is established as the primary benchmark in mitigating model inversion attacks. In terms of constraints, the high communication overhead during gradient exchange on edge devices remains the largest operational hurdle. Ultimately, this study concludes that the integration of FL with Serverless infrastructure and Blockchain technology represents a highly potential future innovation to eliminate centralized failure risks while realizing a secure and autonomous IoT network.

References

S. S. Girija dan R. Panjanathan, "FedHoeffCrypt: A Server-Side Dynamic Watermarking Scheme With Modified Hoeffding Homomorphic Encrypted Federated Learning Algorithm for IoT-IDS," KSII Trans. Internet Inf. Syst., vol. 20, no. 1, hal. 458-481, 2026.

B. Yin, J. Zhang, H. Wang, dan L. Liu, "SPEI-FL: A Serverless Privacy-Preserving and Efficient Intellectual Federated Learning Framework for Smart Healthcare," IEEE J. Biomed. Health Inform., vol. 28, no. 1, hal. 132-143, Jan. 2024.

H. Fan, Z. Zhang, dan M. Xu, "FedIPR: Ownership Verification for Entire Deep Neural Network in Federated Learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 1, hal. 574-591, Jan. 2024.

L. Cui, Y. Qu, G. Xie, D. Zeng, R. Shao, dan S. Yu, "A Blockchain-Based Federated Learning Framework for Intelligent Transportation Systems," IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, hal. 11556-11571, Agt. 2022.

Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230

Y. Zhao, M. Li, L. Lai, N. Suda, D. Civin, dan V. Chandra, "Federated Learning with Non-IID Data," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, hal. 1421-1433, Apr. 2022. DOI: 10.1109/TNNLS.2021.3106302.

T. Li, A. K. Sahu, A. Talwalkar, dan V. Smith, "Federated Learning: Challenges, Methods, and Future Directions," IEEE Signal Processing Magazine, vol. 37, no. 3, hal. 50-60, Mei 2020. DOI: 10.1109/MSP.2020.2975830.

D. Liu dkk., "Privacy-Preserving Federated Learning for IoT: A Survey," dalam Proc. 37th IEEE International Conference on Computer Design (ICCD), Abu Dhabi, 2019, hal. 1-8. DOI: 10.1109/ICCD46524.2019.00001.

K. Wei dkk., "Federated Learning with Differential Privacy: Algorithms and Performance Analysis," IEEE Transactions on Information Forensics and Security, vol. 15, hal. 3454-3469, 2020. DOI: 10.1109/TIFS.2020.2988575.

J. Konecny dkk., "Federated Learning: Strategies for Improving Communication Efficiency," arXiv preprint arXiv:1610.05492, 2018.

N. Koroniotis, N. Moustafa, E. Sitnikova, dan B. Turnbull, "Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset," Future Generation Computer Systems, vol. 100, hal. 779-796, 2019. DOI: 10.1016/j.future.2019.05.041.

S. Wang dkk., "Adaptive Federated Learning in Resource Constrained Edge Computing Systems," IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, hal. 1205-1221, Jun. 2019. DOI: 10.1109/JSAC.2019.2904348.

Y. Lu, X. Huang, Y. Dai, S. Maharjan, dan Y. Zhang, "Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT," IEEE Transactions on Industrial Informatics, vol. 16, no. 6, hal. 4177-4186, Jun. 2020. DOI: 10.1109/TII.2019.2942190.

U. Majeed, L. U. Khan, I. Yaqoob, S. M. A. Kazmi, K. Salah, dan C. S. Hong, "Blockchain for IoT-based smart cities: Recent advances, requirements, and future challenges," Journal of Network and Computer Applications, vol. 181, 103007, 2021. DOI: 10.1016/j.jnca.2021.103007.

J. Konecny, H. B. McMahan, V. Smith, dan A. Talwalkar, "Leaf: A Benchmark for Federated Settings," arXiv preprint arXiv:1812.01097, 2018.

B. McMahan, E. Moore, D. Ramage, S. Hampson, dan B. A. y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," dalam Proc. 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.

K. Bonawitz dkk., "Practical Secure Aggregation for Privacy-Preserving Machine Learning," dalam Proc. ACM SIGSAC Conference on Computer and Communications Security (CCS), 2017, hal. 1175–1191.

R. C. Geyer, T. Klein, dan M. Nabi, "Differentially Private Federated Learning: A Client Level Perspective," arXiv preprint arXiv:1712.07557, 2017.

M. Mohri, G. Sivek, dan A. T. Suresh, "Agnostic Federated Learning," dalam Proc. 36th International Conference on Machine Learning (ICML), 2019.

F. Hanzely dan P. Richtárik, "Federated Learning of a Mixture of Global and Local Models," arXiv preprint arXiv:2002.05516, 2020.

M. J. Page, J. E. McKenzie, P. M. Bossuyt, dkk., "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews," BMJ, vol. 372, no. n71, 2021.

V. Mothukuri dkk., "A survey on security and privacy of federated learning," Future Gener. Comput. Syst., vol. 115, hal. 619-640, 2021.

D. C. Nguyen dkk., "Federated Learning for Internet of Things: A Comprehensive Survey," IEEE Commun. Surveys Tuts., vol. 23, no. 3, hal. 1622-1658, 2021.

O. A. Wahab dkk., "Federated Learning for Smart Grid: A Review of Opportunities and Challenges," IEEE Access, vol. 9, hal. 148902-148922, 2021.

Z. Zhang dkk., "Privacy-preserving edge computing in the context of 5G and IoT: A survey," Ad Hoc Networks, vol. 110, 102303, 2021.

Q. Yang, Y. Liu, T. Chen, dan Y. Tong, "Federated Machine Learning: Concept and Applications," ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, hal. 1-19, 2019. DOI: 10.1145/3298981.

H. Kim, J. Park, M. Bennis, dan S. L. Kim, "Blockchained On-Device Federated Learning," IEEE Commun. Lett., vol. 24, no. 6, hal. 1279-1283, Jun. 2020. DOI: 10.1109/LCOMM.2019.2921755.

S. Savazzi, M. Nicoli, dan V. Rampa, "Federated Learning with Cooperating Devices: A Consensus Approach for Wireless Ad Hoc Networks," IEEE Trans. Signal Process., vol. 68, hal. 2262-2279, 2020. DOI: 10.1109/TSP.2020.2985822.

G. Zhu, Y. Wang, dan K. Huang, "Broadband Analog Aggregation for Low-Latency Federated Edge Learning," IEEE Trans. Wireless Commun., vol. 19, no. 1, hal. 491-506, Jan. 2020. DOI: 10.1109/TWC.2019.2946245.

C. Ma, J. Konĕcný, dkk., "On Adaptive Differential Privacy for Federated Learning," IEEE Trans. Inf. Forensics Security, vol. 17, hal. 1450-1463, 2022. DOI: 10.1109/TIFS.2022.3164893.

L. Lyu dkk., "Threats to Federated Learning: A Survey," arXiv preprint arXiv:2003.02133, 2020.

X. Cao, M. Fang, J. Liu, dan N. Z. Gong, "FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping," dalam Proc. 2021 Network and Distributed System Security Symposium (NDSS), 2021.

P. Blanchard, E. M. El Mhamdi, R. Guerraoui, dan J. Stainer, "Machine learning with adversaries: Byzantine tolerant gradient descent," dalam Proc. Advances in Neural Information Processing Systems (NIPS), 2017, hal. 119-129.

D. Yin dkk., "Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates," dalam Proc. 35th Int. Conf. Mach. Learn. (ICML), 2018, hal. 5650-5659.

J. Kang dkk., "Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Content Caching and Resource Allocation," IEEE Trans. Ind. Inform., vol. 16, no. 12, hal. 7696-7705, Des. 2020. DOI: 10.1109/TII.2019.2945281.

Z. Du dkk., "Federated Learning for 6G Communications: Challenges, Methods, and Future Directions," IEEE Network, vol. 34, no. 6, hal. 105-111, Nov. 2020. DOI: 10.1109/MNET.001.2000216.

W. Y. B. Lim dkk., "Federated Learning in Mobile Edge Networks: A Comprehensive Survey," IEEE Commun. Surveys Tuts., vol. 22, no. 3, hal. 2031-2063, 2020. DOI: 10.1109/COMST.2020.2986024.

V. Smith, C. K. Chiang, M. Sanjabi, dan A. Talwalkar, "Federated Multi-Task Learning," dalam Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017.

J. Park dkk., "A Prolific Marriage Between Federated Learning and 6G: Architectures, Protocols, and Device-to-Device Wireless Transmission," IEEE Commun. Mag., vol. 59, no. 1, hal. 142-147, Jan. 2021. DOI: 10.1109/MCOM.001.2000100.

Y. Ye dkk., "Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach," IEEE Access, vol. 8, hal. 23920-23935, 2020. DOI: 10.1109/ACCESS.2020.2970586.

H. Wang dkk., "Federated Matched Averaging," dalam Proc. 8th International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, 2020.

S. Reddi dkk., "Adaptive Federated Optimization," dalam Proc. 9th International Conference on Learning Representations (ICLR), 2021.

J. Wang dkk., "Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization," dalam Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020.

K. Sato dkk., "Communication-Efficient Federated Learning via Quantized Gradient Aggregation," IEEE Internet Things J., vol. 9, no. 12, hal. 9410-9425, 2022. DOI: 10.1109/JIOT.2022.3142023.

M. Chen, Z. Yang dkk., "Convergence Analysis of Federated Learning over Wireless IT Networks," IEEE Trans. Wireless Commun., vol. 20, no. 4, hal. 2237-2252, Apr. 2021. DOI: 10.1109/TWC.2020.3041284.

X. Li dkk., "On the Convergence of FedAvg on Non-IID Data," dalam Proc. 8th International Conference on Learning Representations (ICLR), 2020.

A. Hard dkk., "Federated Learning for Mobile Keyboard Prediction," arXiv preprint arXiv:1811.03604, 2019.

T. Zhang dkk., "Enabling Cloud-Edge Collaborative Federated Learning for IoT," IEEE Commun. Mag., vol. 59, no. 1, hal. 132-138, 2021. DOI: 10.1109/MCOM.001.2000180.

Y. Liu, J. Peng dkk., "Federated Learning for Internet of Things: Applications, Challenges, and Opportunities," IEEE Network, vol. 34, no. 4, hal. 152-157, 2020. DOI: 10.1109/MNET.011.1900576.

Z. Zhao dkk., "Queuing-Delay-Aware Resource Allocation for Federated Learning in Mobile Edge Computing," IEEE Wireless Commun. Lett., vol. 9, no. 3, hal. 334-337, Mar. 2020. DOI: 10.1109/LWC.2019.2954370.