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A fair and verifiable federated learning profit-sharing scheme

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Abstract

In recent years, gradient boosting decision trees (GBDTs) has become a popular machine learning algorithm and there have been some studies on federated GBDT training to preserve clients’ privacy. However, existing schemes face some severe issues. For example, the integrity of the training process cannot be guaranteed. And most of the schemes ignore how to evaluate the performance gains from different clients’ datasets fairly. Developing a fair and secure contribution evaluation mechanism in federated learning to motivate clients to join federated learning remains a challenge. In this paper, we propose a fair and verifiable secure federated GBDT scheme that utilizes Trusted Execution Environments (TEEs) to ensure the integrity of the GBDT training process and quantify the contribution of different parties fairly. We propose a fair and verifiable contribution calculation mechanism based on TEE and the adaptive truncated Monte Carlo approximation Shapley value method. The mechanism can adapt to the limited resources of the device and avoid dishonest behaviors during the training process. In addition, as far as we all know, we attempted to implement the validation of contributions in the federated GBDT scheme for the first time. We implement a prototype of our scheme and evaluate it comprehensively. The results show that, compared with calculating the contribution of each party by the Shapley value method, our scheme can significantly improve the efficiency of contribution calculation in the case of more parties, and provide integrity and fairness guarantees for model and contribution calculations.

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Notes

  1. https://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/

References

  1. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2021). Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1—-2), 1–210.

    Article  Google Scholar 

  2. Zhang, J., Li, C., Robles-Kelly, A., & Kankanhalli, M.(2020). Hierarchically fair federated learning. arXiv preprint arXiv:2004.10386.

  3. Huang, L., Yin, Y., Fu, Z., Zhang, S., Deng, H., & Liu, D. (2020). Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data. Plos one, 15(4), 0230706.

    Google Scholar 

  4. Tian, Z., Zhang, R., Hou, X., Liu, J., & Ren, K. (2020). Federboost: Private federated learning for gbdt. arXiv preprint arXiv:2011.02796.

  5. Liu, Y., Ma, Z., Liu, X., Ma, S., Nepal, S., Deng, R. H., & Ren, K.(2020). Boosting privately: Federated extreme gradient boosting for mobile crowdsensing. In 2020 IEEE 40th international conference on distributed computing systems (ICDCS), (pp. 1–11). IEEE.

  6. Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., Papadopoulos, D., & Yang, Q. (2021). Secureboost: A lossless federated learning framework. IEEE Intelligent Systems, 36(6), 87–98.

    Article  Google Scholar 

  7. Zhao, L., Ni, L., Hu, S., Chen, Y., Zhou, P., Xiao, F., & Wu, L. (2018). Inprivate digging: Enabling tree-based distributed data mining with differential privacy. In IEEE INFOCOM 2018-IEEE conference on computer communications, (pp. 2087–2095). IEEE.

  8. Li, Q., Wen, Z., & He, B. (2020). Practical federated gradient boosting decision trees. In Proceedings of the AAAI conference on artificial intelligence, (pp. 4642–4649, vol. 34).

  9. Wang, G., Dang, C. X., & Zhou, Z. (2019). Measure contribution of participants in federated learning. In 2019 IEEE international conference on big data (Big Data) (pp. 2597–2604). IEEE.

  10. Ghorbani, A., & Zou, J. (2019). Data shapley: Equitable valuation of data for machine learning. In International conference on machine learning, (pp. 2242–2251). PMLR.

  11. McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, (pp. 1273–1282). PMLR.

  12. Ghareh, C. J., & Dimitrios, P. (2020). Mitigating leakage in federated learning with trusted hardware. arXiv preprint arXiv:2011.04948.

  13. Wu, Y., Cai, S., Xiao, X., Chen, G., & Ooi, B. C. (2020). Privacy preserving vertical federated learning for tree-based models. arXiv preprint arXiv:2008.06170.

  14. Jia, R., Dao, D., Wang, B., Hubis, F. A., Hynes, N., Gürel, N. M., Li, B., Zhang, C., Song, D., & Spanos, C. J. (2019). Towards efficient data valuation based on the shapley value. In The 22nd international conference on artificial intelligence and statistics, (pp. 1167–1176). PMLR.

  15. Sim, R. H. L., Zhang, Y., Chan, M. C., & Low, B. K. H. (2020). Collaborative machine learning with incentive-aware model rewards. In International conference on machine learning, (pp. 8927–8936). PMLR.

  16. Song, T., Tong, Y., & Wei, S. (2019). Profit allocation for federated learning. In 2019 IEEE international conference on big data (Big Data) (pp. 2577–2586). IEEE.

  17. Lim, W. Y. B., Xiong, Z., Miao, C., Niyato, D., Yang, Q., Leung, C., & Poor, H. V. (2020). Hierarchical incentive mechanism design for federated machine learning in mobile networks. IEEE Internet of Things Journal, 7(10), 9575–9588.

    Article  Google Scholar 

  18. Yu, H., Liu, Z., Liu, Y., Chen, T., Cong, M., Weng, X., Niyato, D., & Yang, Q. (2020). A sustainable incentive scheme for federated learning. IEEE Intelligent Systems, 35(4), 58–69.

    Article  Google Scholar 

  19. Subramanyan, P., Sinha, R., Lebedev, I., Devadas, S., & Seshia, S. A. (2017). A formal foundation for secure remote execution of enclaves. In Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, (pp. 2435–2450).

  20. Intel, R. (2012). Architecture instruction set extensions programming reference. Intel Corporation, Feb.

  21. Costan, V., & Devadas, S. (2016). Intel sgx explained. Cryptology ePrint Archive.

  22. Mo, F., Haddadi, H., Katevas, K., Marin, E., Perino, D., & Kourtellis, N. (2021). Ppfl: privacy-preserving federated learning with trusted execution environments. In Proceedings of the 19th annual international conference on mobile systems, applications, and services, (pp. 94–108).

  23. Yan, H., Hu, L., Xiang, X., Liu, Z., & Yuan, X. (2021). Ppcl: Privacy-preserving collaborative learning for mitigating indirect information leakage. Information Sciences, 548, 423–437.

    Article  MathSciNet  Google Scholar 

  24. Tramer, F., & Boneh, D. (2018). Slalom: Fast, verifiable and private execution of neural networks in trusted hardware. arXiv preprint arXiv:1806.03287.

  25. Cheng, R., Zhang, F., Kos, J., He, W., Hynes, N., Johnson, N., Juels, A., Miller, A., & Song, D. (2019). Ekiden: A platform for confidentiality-preserving, trustworthy, and performant smart contracts. In 2019 IEEE European symposium on security and privacy (EuroS &P), (pp. 185–200). IEEE.

  26. Hanzlik, L., Zhang, Y., Grosse, K., Salem, A., Augustin, M., Backes, M., & Fritz, M. (2021). Mlcapsule: Guarded offline deployment of machine learning as a service. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, (pp. 3300–3309).

  27. Chen, Y., Luo, F., Li, T., Xiang, T., Liu, Z., & Li, J. (2020). A training-integrity privacy-preserving federated learning scheme with trusted execution environment. Information Sciences, 522, 69–79.

    Article  Google Scholar 

  28. Zhang, X., Li, F., Zhang, Z., Li, Q., Wang, C., & Wu, J. (2020). Enabling execution assurance of federated learning at untrusted participants. In IEEE INFOCOM 2020-IEEE conference on computer communications, (pp. 1877–1886). IEEE.

  29. Zhang, Y., Wang, Z., Cao, J., Hou, R., & Meng, D. (2021). Shufflefl: gradient-preserving federated learning using trusted execution environment. In Proceedings of the 18th ACM international conference on computing Frontiers, (pp. 161–168).

  30. Kuhn, H. W., & Tucker, A. W. (1953). Contributions to the theory of games vol. 28. ???: Princeton University Press.

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Acknowledgements

The work is partially supported by the Guangxi ”Bagui Scholar” Teams for Innovation and Research Project, the National Natural Science Foundation of China (No. 61672176), the Center for Applied Mathematics of Guangxi (Guangxi Normal University), the Guangxi Science and technology project (GuikeAA22067070 and GuikeAD21220114), the Guangxi Science and Technology Plan Projects No. AD20159039, the Guangxi Talent Highland Project of Big Data Intelligence and Application.

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  1. School of Computer Science and Engineering and School of Software, Guangxi Normal University, 530022, Guilin, China

    Xianxian Li, Mei Huang, Shiqi Gao & Zhenkui Shi

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  1. Xianxian Li
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Correspondence to Zhenkui Shi.

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Li, X., Huang, M., Gao, S. et al. A fair and verifiable federated learning profit-sharing scheme. Wireless Netw (2022). https://doi.org/10.1007/s11276-022-03110-w

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