Abstract
Vertical federated learning (VFL) is a distributed machine learning technology that is suitable for model building in organizations across different industries. It enables the identification of a common set of data that co-occur across organizations. However, VFL uses private set intersection (PSI) protocols, which requires making all data shareable, and satisfying the data minimization principle in the General Data Protection Regulation is difficult. To mitigate noncompliance in privacy regulations, we propose a new VFL method that uses horizontal federated learning to identify the common set instead of PSI. The method consists of two concepts: The first is to use a common data structure between organizations to avoid using PSI. The second is to identify the common set from machine learning classifiers of unseen data of a certain class. Our proposed method considers that the data labeled as the desired class is unseen data and it is not in the common set. Experimental results show that the F-measure is 0.8 or higher in 40% of the common set ratios.
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
References
Bendale, A., Boult, T.E.: Towards open set deep networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1563–1572. IEEE Computer Society, Los Alamitos, CA, USA (2016). https://doi.org/10.1109/CVPR.2016.173
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970). https://doi.org/10.1145/362686.362692
Dhamija, A.R., Günther, M., Boult, T.E.: Reducing network agnostophobia. In: NeurIPS, pp. 9175–9186 (2018). https://proceedings.neurips.cc/paper/2018/hash/48db71587df6c7c442e5b76cc723169a-Abstract.html
Egert, R., Fischlin, M., Gens, D., Jacob, S., Senker, M., Tillmanns, J.: Privately computing set-union and set-intersection cardinality via bloom filters. In: Foo, E., Stebila, D. (eds.) ACISP 2015. LNCS, vol. 9144, pp. 413–430. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19962-7_24
Jiang, J.C., Kantarci, B., Oktug, S., Soyata, T.: Federated learning in smart city sensing: challenges and opportunities. Sensors 20(21), 6230 (2020). https://doi.org/10.3390/s20216230
Kairouz, P., et al.: Advances and open problems in federated learning (2019). https://doi.org/10.48550/ARXIV.1912.04977
Kholod, I., et al.: Open-source federated learning frameworks for IoT: a comparative review and analysis. Sensors 21(1), 167 (2021). https://doi.org/10.3390/s21010167
Matan, O., et al.: Handwritten character recognition using neural network architectures. In: the 4th USPS Advanced Technology Conference, pp. 1003–1011 (1990)
Miyaji, A., Nagao, Y.: Privacy preserving data integration protocol. In: 2020 15th Asia Joint Conference on Information Security (AsiaJCIS), pp. 89–96 (2020). https://doi.org/10.1109/AsiaJCIS50894.2020.00025
OpenMined: Pysyft (2022). https://www.openmined.org/
Perera, P., Oza, P., Patel, V.M.: One-class classification: a survey. arXiv preprint arXiv:2101.03064 (2021)
Shu, L., Xu, H., Liu, B.: DOC: deep open classification of text documents. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2911–2916. Association for Computational Linguistics, Copenhagen, Denmark (2017). https://doi.org/10.18653/v1/D17-1314
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), 1–19 (2019). https://doi.org/10.1145/3298981
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Someda, H., Osada, S., Kajikawa, Y. (2023). Application of Probabilistic Common Set on an Open World Set for Vertical Federated Learning. In: Takizawa, H., Shen, H., Hanawa, T., Hyuk Park, J., Tian, H., Egawa, R. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2022. Lecture Notes in Computer Science, vol 13798. Springer, Cham. https://doi.org/10.1007/978-3-031-29927-8_39
Download citation
DOI: https://doi.org/10.1007/978-3-031-29927-8_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29926-1
Online ISBN: 978-3-031-29927-8
eBook Packages: Computer ScienceComputer Science (R0)