Abstract
Recently, as big data and AI technology advance, data privacy and security are increasingly critical. Federated Learning (FL) has become a key solution in machine learning to address these concerns. In this paper, we present a secure and lightweight FL scheme. It employs masking and Secret Sharing (SS) to securely aggregate data from distributed clients, thereby reducing the demands of model training on system resources. The scheme also computes data similarity among clients to evaluate each client’s contribution, defending against challenges posed by malicious clients. This approach safeguards privacy, facilitates accurate model updates, and addresses the challenges of limited resources in edge computing environments. We subjected our framework to rigorous validation using MNIST datasets. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., Guizani, M.: A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476–5497 (2021)
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., Zhang, W.: A survey on federated learning: challenges and applications. Int. J. Mach. Learn. Cybern. 14(2), 513–535 (2023)
Ratnayake, H., Chen, L., Ding, X.: A review of federated learning: taxonomy, privacy and future directions. J. Intell. Inf. Syst. 61, 1–27 (2023)
Yin, X., Zhu, Y., Hu, J.: A taxonomy, review, and future directions, a comprehensive survey of privacy-preserving federated learning. ACM Comput. Surv. 54, 1–36 (2021)
Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M.V., Herrera, F., Martínez-Cámara, E.: Survey on federated learning threats: concepts, taxonomy on attacks and defences, experimental study and challenges. Inf. Fusion 90, 148–173 (2023)
Sattler, F., Müller, K.-R., Samek, W.: Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans. Neural Netw. Learn. Syst. (TNNLS) 32(8), 3710–3722 (2021)
Qin, Z., Deng, S., Zhao, M., Yan, X.: FedAPEN: personalized cross-silo federated learning with adaptability to statistical heterogeneity. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1954–1964 (2023)
Rebuffi, S.-A., Gowal, S., Calian, D.A., Stimberg, F., Wiles, O., Mann, T.A.: Data augmentation can improve robustness. Adv. Neural. Inf. Process. Syst. 34, 29935–29948 (2021)
Wang, F., Li, B., Li, B.: Federated unlearning and its privacy threats. IEEE Netw., 1–7 (2023)
Fang, H., Qian, Q.: Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet 13(4), 94 (2021)
Fazli Khojir, H., Alhadidi, D., Rouhani, S., Mohammed, N.: FedShare: secure aggregation based on additive secret sharing in federated learning. In: Proceedings of the 27th International Database Engineered Applications Symposium, pp. 25–33 (2023)
Ma, J., Naas, S.-A., Sigg, S., Lyu, X.: Privacy-preserving federated learning based on multi-key homomorphic encryption. Int. J. Intell. Syst. 37, 5880–5901 (2022)
Xie, Y., Chen, B., Zhang, J., Wu, D.: Defending against membership inference attacks in federated learning via adversarial example. In: International Conference on Mobility, Sensing and Networking (MSN), pp. 153–160. IEEE (2021)
Ren, H., Deng, J., Xie, X.: GRNN: generative regression neural network-a data leakage attack for federated learning. ACM Trans. Intell. Syst. Technol. (TIST) 13(4), 1–24 (2022)
Truex, S., et al.: A hybrid approach to privacy-preserving federated learning. In: ACM Workshop on Artificial Intelligence and Security, pp. 1–11 (2019)
Qammar, A., Karim, A., Ning, H., Ding, J.: Securing federated learning with blockchain: a systematic literature review. Artif. Intell. Rev. 56(5), 3951–3985 (2023)
Zhang, Z., et al.: LSFL: a lightweight and secure federated learning scheme for edge computing. IEEE Trans. Inf. Forensics Secur. 18, 365–379 (2022)
So, J., et al.: LightSecAgg: a lightweight and versatile design for secure aggregation in federated learning. Proc. Mach. Learn. Syst. 4, 694–720 (2022)
Cao, Z., et al.: Privacy matters: vertical federated linear contextual bandits for privacy protected recommendation. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 154–166 (2023)
Wei, Z., Pei, Q., Zhang, N., Liu, X., Celimuge, W., Taherkordi, A.: Lightweight federated learning for large-scale IoT devices with privacy guarantee. IEEE Internet Things J. 10, 3179–3191 (2021)
Yang, C., et al.: RaftFed: a lightweight federated learning framework for vehicular crowd intelligence. arXiv preprintarXiv:2310.07268 (2023)
Guo, Y., Wu, Y., Zhu, Y., Yang, B., Han, C.: Anomaly detection using distributed log data: a lightweight federated learning approach. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2021)
Zhu, W., Goudarzi, M., Buyya, R.: Flight: a lightweight federated learning framework in edge and fog computing. arXiv preprintarXiv:2308.02834 (2023)
Meng, D., Li, H., Zhu, F., Li, X.: FedMONN: meta operation neural network for secure federated aggregation. In: IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 579–584 (2020)
Qin, Z., Yao, L., Chen, D., Li, Y., Ding, B., Cheng, M.: Revisiting personalized federated learning: Robustness against backdoor attacks. arXiv preprintarXiv:2302.01677 (2023)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Hsu, T.-M.H., Qi, H., Brown, M.: Measuring the effects of non-identical data distribution for federated visual classification. arXiv preprintarXiv:1909.06335 (2019)
Shejwalkar, V., Houmansadr, A., Kairouz, P., Ramage, D.: Back to the drawing board: a critical evaluation of poisoning attacks on production federated learning. In: 2022 IEEE Symposium on Security and Privacy (SP), pp. 1354–1371. IEEE (2022)
Fang, M., Cao, X., Jia, J., Gong, N.: Local model poisoning attacks to \(\{\)Byzantine-Robust\(\}\) federated learning. In: 29th USENIX Security Symposium (USENIX Security 2020), pp. 1605–1622 (2020)
Acknowledgements
The research of the first author is partially supported by the Japan Science and Technology Agency, Support for Pioneering Research Initiated by the Next Generation (JST SPRING) under Grant JPMJSP2136. The research of the second author is partially supported by the International Exchange, Foreign Researcher Invitation Program of National Institute of Information and Communications Technology (NICT), Japan. The first and fourth authors are funded by JSPS international scientific exchanges between Japan and India, Bilateral Program DTS-JSP, grant number JPJSBP120227718.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, C., Wang, K.IK., Li, P., Sakurai, K. (2024). Enhancing Security and Efficiency: A Lightweight Federated Learning Approach. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 202. Springer, Cham. https://doi.org/10.1007/978-3-031-57916-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-57916-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57915-8
Online ISBN: 978-3-031-57916-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)