Abstract
Federated learning is a paradigm of distributed machine learning that enables multiple participants to collaboratively train a global model while preserving data privacy and locality. However, federated learning faces the dual challenges of data privacy and model fairness. Balancing these requirements while achieving efficient and robust learning outcomes remains a pressing issue. In this paper, based on the Universal Composable framework, we introduce an ideal federated learning functionality \( F_{\text {FSFL}} \) and a fair and secure federated learning real-world protocol \( \pi _{FSFL} \). Our protocol integrates differential privacy and fairness incentive mechanisms, safeguarding client data privacy and countering potential threats from dishonest or malicious clients to model fairness. We demonstrate that our protocol can securely simulate the ideal functionality \( F_{\text {FSFL}} \) under the semi-honest model and resist passive attacks from polynomial-time adversaries. We further conduct a series of experiments on three widely used datasets (MNIST, CIFAR-10, and FashionMNIST) to validate the efficacy and robustness of our protocol under varying noise levels and malicious client ratios. Experimental results reveal that compared to other federated learning protocols, our method ensures data privacy and model fairness while delivering performance on par with, if not better than, baseline protocols.
Supported by organizations the National Natural Science Foundation of China (62002080), Guizhou Province Science and Technology Plan Project for 2023 (Guizhou Province Science Foundation - General [2023] No. 440), Project for Improving the Quality of Universities in Municipalities and Provinces (Ministry Office Issued [2022] No. 10-32), Natural Science Research Project of Guizhou Provincial Department of Education (Guizhou Education Union KY [2020] No. 179, No. 180, [2021] No. 140), Major Special Project Plan of Science and Technology in Guizhou Province (20183001), School-level Project of Kaili University (2022YB08), and School-level Research Project of Guizhou University of Finance and Economics (2020XYB02).
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References
Ahmed, S., Miskon, S.: IoT driven resiliency with artificial intelligence, machine learning and analytics for digital transformation. In: 2020 International Conference on Decision Aid Sciences and Application (DASA), pp. 1205–1208. IEEE (2020)
Hosseinalipour, S., Brinton, C.G., Aggarwal, V., et al.: From federated to fog learning: distributed machine learning over heterogeneous wireless networks. IEEE Commun. Mag. 58(12), 41–47 (2020)
Konečný, J., McMahan, H.B., Yu, F.X., et al.: Federated learning: strategies for improving communication efficiency. ar**v preprint ar**v:1610.05492 (2016)
Li, T., Sanjabi, M., Beirami, A., et al.: Fair resource allocation in federated learning. ar**v preprint ar**v:1905.10497 (2019)
Dorner, F.E., Konstantinov, N., Pashaliev, G., et al.: Incentivizing honesty among competitors in collaborative learning and optimization]. ar**v preprint ar**v:2305.16272 (2023)
Ro, J., Chen, M., Mathews, R., et al.: Communication-efficient agnostic federated averaging (2021). https://doi.org/10.48550/ar**v.2104.02748
Ji, Y., Kou, Z., Zhong, X., et al.: Client selection and bandwidth allocation for federated learning: an online optimization perspective (2022). https://doi.org/10.48550/ar**v.2205.04709
Woo, G., Kim, H., Park, S., You, C., Park, H.: Fairness-based multi-AP coordination using federated learning in Wi-Fi 7. Sensors 22(24), 9776 (2022). https://doi.org/10.3390/s22249776
Liu, J., et al.: Multi-job intelligent scheduling with cross-device federated learning. IEEE Trans. Parallel Distrib. Syst. 34(2), 535–551 (2023). https://doi.org/10.1109/TPDS.2022.3224941
Arouj, A., Abdelmoniem, A.M.: Towards energy-aware federated learning on battery-powered clients. ar**v e-prints (2022). https://doi.org/10.48550/ar**v.2208.04505
Llasag Rosero, R., Silva, C., Ribeiro, B.: Forecasting functional time series using federated learning. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds.) EANN 2023. CCIS, vol. 1826, pp. 491–504. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34204-2_40
Lim, W.Y.B., et al.: Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun. Surv. Tutorials 22(3), 2031–2063 (2020). https://doi.org/10.1109/COMST.2020.2986024
Lyu, L., Xu, X., Wang, Q., et al.: Collaborative fairness in federated learning, pp. 189–204. Privacy and Incentive, Federated Learning (2020)
Du, W., Xu, D., Wu, X., et al.: Fairness-aware agnostic federated learning. Proceedings (2020). https://doi.org/10.48550/ar**v.2010.05057
Ezzeldin, Y.H., Yan, S., He, C., et al.: Fairfed: enabling group fairness in federated learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 37. no. 6, pp. 7494–7502 (2023)
Shejwalkar, V., Houmansadr, A.: Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning. In: Proceedings 2021 Network and Distributed System Security Symposium (2021). https://doi.org/10.14722/NDSS.2021.24498
Javed, A.R., Hassan, M.A., Shahzad, F., et al.: Integration of blockchain technology and federated learning in vehicular (IoT) networks: a comprehensive survey. Sensors 22(12), 4394 (2022). https://doi.org/10.3390/s22124394
Liu, J., He, X., Sun, R., et al.: Privacy-preserving data sharing scheme with FL via MPC in financial permissioned blockchain. In: ICC 2021-IEEE International Conference on Communications, pp. 1–6. IEEE (2021). https://doi.org/10.1109/ICC42927.2021.9500868
Zhao, J., et al.: Privacy-enhanced federated learning: a restrictively self-sampled and data-perturbed local differential privacy method. Electronics 11(23), 4007 (2022). https://doi.org/10.3390/electronics11234007
Cao, X., Zhang, Z., Jia, J., et al.: Flcert: provably secure federated learning against poisoning attacks. IEEE Trans. Inf. Forensics Secur. 17, 3691–3705 (2022)
Li, Z., He, Y., Yu, H., et al.: Data heterogeneity-robust federated learning via group client selection in industrial IoT. IEEE Internet Things J. 9(18), 17844–17857 (2022)
Zhang, W., Wang, X., Zhou, P., et al.: Client selection for federated learning with non-IID data in mobile edge computing. IEEE Access 9, 24462–24474 (2021)
Luo, B., **ao, W., Wang, S., et al.: Tackling system and statistical heterogeneity for federated learning with adaptive client sampling. In: IEEE INFOCOM 2022-IEEE Conference on Computer Communications, pp. 1739–1748. IEEE (2022)
Ferrag, M.A., Friha, O., Hamouda, D., et al.: Edge-IIoTset: a new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning. IEEE Access 10, 40281–40306 (2022)
ur Rehman, M.H, Dirir, A.M., Salah, K., et al.: TrustFed: a framework for fair and trustworthy cross-device federated learning in IIoT. IEEE Trans. Ind. Inform. 17(12), 8485–8494 (2021)
Cheng, Z., Jiang, Y., Huang, X., et al.: Universal interactive verification framework for federated learning protocol. In: Proceedings of the 2021 10th International Conference on Networks, Communication and Computing, pp. 108–113 (2021)
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Qiuxian, L., Quanxing, Z., Hongfa, D. (2024). A Secure and Fair Federated Learning Protocol Under the Universal Composability Framework. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14554. Springer, Cham. https://doi.org/10.1007/978-3-031-53305-1_35
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