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DP-FL: a novel differentially private federated learning framework for the unbalanced data

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Abstract

Security issues of artificial intelligence attract many attention in many research fields and industries, such as face recognition, medical care, and client services. Federated learning is proposed by Google, which can prevent the leakage of data during the AI training because each enterprise only needs to exchange training parameters without data sharing. In this paper, we present a novel differentially private federated learning framework (DP-FL) for unbalanced data. In the cloud server, DP-FL framework considers the unbalanced data of different users to set different privacy budgets. In the user client, we design a novel differential private convolutional neural networks with adaptive gradient descent (DPAGD-CNN) algorithm to update each user’s training parameters. Experimental results on several real-world datasets demonstrate that the DF-FL framework can protect data privacy with higher accuracy than existing works.

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References

  1. Abadi, M., Chu, A., Goodfellow I., McMahan, H., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 23th ACM Conference on Computer and Communications Security. ACM (2016)

  2. Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., Shmatikov, V.: How to backdoor federated learning (2018)

  3. Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H., et al: Towards federated learning at scale: system design. arXiv:1902.01046 (2019)

  4. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H., Patel, S., Ramage, D., Segal, A., Seth, K.: Practical secure aggregation for privacy preserving machine learning. In: ACM Conference on Computer and Communications Security (ACM CCS) (2016)

  5. Dwork, C.: Differential privacy. In: Proceedings of the 33rd International Colloquium on Automata, Languages and Programming, Venice, Italy, pp 1–12 (2006)

  6. Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9(3–4), 211–407 (2014)

    MathSciNet  MATH  Google Scholar 

  7. Du, W., Han, Y., Chen, S.: Privacy-preserving Multivariate Statistical Analysis: Linear Regression and Classification. In: Proceedings of the 2004 SIAM international conference on data mining. pp. 222–233 (2004)

  8. Geyer, R., Klein, T., Nabi, M.: Differentially private federated learning: a client level perspective. NIPS Workshop: Machine Learning on the Phone and other Consumer Devices (2017)

  9. Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., Ramage, D.: Federated learning for mobile keyboard prediction. arXiv:1811.03604 (2018)

  10. Huang, X., Liao, Q., Qi, S., Guan, J., Jiang, Z., Wang, X.: Differentially Private Convolutional Neural Networks with Adaptive Gradient Descent. IEEE International Conference on Data Science in Cyberspace (DSC). Hangzhou, China (2018)

  11. Konečny, J., McMahan, H., Ramage, D., Richtarik P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv:1610.02527 (2016)

  12. Konečny, J., McMahan, H., Yu, F., Richtárik, P., Bacon, D.: Federated learning: strategies for improving communication efficiency. arXiv:1610.05492 (2016)

  13. Lee, J., Kifer, D.: Concentrated differentially private gradient descent with adaptive per-iteration privacy budget. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1656–1665 (2018)

  14. Li, N., Li, T., Venkatasubramanian S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: Proceedings of the IEEE International Conference on Data Engineering(ICDE). Istanbul, Turkey, pp 106–115 (2007)

  15. Liu, Y., Chen, T., Yang Q.: Secure federated transfer learning. arXiv:1812.03337 (2018)

  16. Machnavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. In: Proceedings of the 22nd International Conference on Data engineering(ICDE). Atlanta, Georgia, USA, pp 24–35 (2006)

  17. McMahan, B., Ramage, D.: Federated learning: Collaborative machine learning without centralized training data. https://ai.googleblog.com/2017/04/federated-learning-collaborative.html, Accessed 04 Oct 2018 (2018)

  18. McMahan, H., Moore, E., Ramage, D., Hampson, S., Arcas, B.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) (2017)

  19. Mohassel, P., Zhang, Y.: Secureml: A System for Scalable Privacy-preserving Machine Learning. In: Proceedings of the 2017 IEEE Symposium on Security and Privacy(SP), pp. 19–38 (2017)

  20. Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), New York, USA, pp 1310–1321 (2015)

  21. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–503 (2016)

    Article  Google Scholar 

  22. Smith, V., Chiang, C., Sanjabi, M., Talwalkar, A.: Federated multi-task learning. In: Advances in Neural Information Processing Systems, pp 4427–4437 (2017)

  23. Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based System 10(5), 24–35 (2002)

    MathSciNet  MATH  Google Scholar 

  24. Wang, W., He, S., Sun, L., Jiang, T., Zhang, Q.: Cross-technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts. IEEE Trans. Wirel. Commun. 18(2), 796–806 (2019)

    Article  Google Scholar 

  25. Wang, W., Zhang, Q.: Privacy-preserving collaborative spectrum sensing with multiple service providers. IEEE Trans. Wirel. Commun. 14(2), 1011–1019 (2014)

    Article  Google Scholar 

  26. Wang, W., Chen, L., Zhang, Q.: Outsourcing high-dimensional healthcare data to cloud with personalized privacy preservation. Comput. Netw. 88, 136–148 (2015)

    Article  Google Scholar 

  27. WeBank: https://github.com/WeBankFinTech/FATE (2020)

  28. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), Article 12 (2019)

  29. Yuan, J., Yu, S.: Privacy Preserving Back-propagation Neural Network Learning Made Practical with Cloud Computing. IEEE Trans. Parallel Distrib. Syst. 25(1), 212–221 (2013)

    Article  Google Scholar 

  30. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-iid data. arXiv:1806.00582 (2018)

Download references

Acknowledgements

This work is partly supported by the National Key Research and Development Program of China under grand No. 2017YFB0802204. National Natural Science Foundation of China under the grand No.61976051 and Basic Research Project of Shenzhen under grant No.JCYJ20180306174743727 and National and provincial program supporting projects of Shenzhen, China No. GJHS20170313113617970.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, 518000, China

    Xixi Huang, Zoe L. Jiang, Shuhan Qi, Xuan Wang & Qing Liao

  2. School of Computer Science and Network Security, Dongguan University of Technology, Dongguan, 523000, China

    Ye Ding

  3. Peng Cheng Laboratory, Shenzhen, 518000, China

    Zoe L. Jiang & Qing Liao

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  1. Xixi Huang
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  2. Ye Ding
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  3. Zoe L. Jiang
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  4. Shuhan Qi
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  5. Xuan Wang
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  6. Qing Liao
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Correspondence to Qing Liao.

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This article belongs to the Topical Collection:Special Issue on Data Science in Cyberspace 2019

Guest Editors: Bin Zhou, Feifei Li and Jinjun Chen

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Huang, X., Ding, Y., Jiang, Z.L. et al. DP-FL: a novel differentially private federated learning framework for the unbalanced data. World Wide Web 23, 2529–2545 (2020). https://doi.org/10.1007/s11280-020-00780-4

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  • DOI: https://doi.org/10.1007/s11280-020-00780-4

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