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Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Collaborative Data Analysis: Non-model Sharing-Type Machine Learning for Distributed Data

Authors : Akira Imakura, Xiucai Ye, Tetsuya Sakurai

Published in: Knowledge Management and Acquisition for Intelligent Systems

Publisher: Springer International Publishing

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Abstract

This paper proposes a novel non-model sharing-type collaborative learning method for distributed data analysis, in which data are partitioned in both samples and features. Analyzing these types of distributed data are essential tasks in many applications, e.g., medical data analysis and manufacturing data analysis due to privacy and confidentiality concerns. By centralizing the intermediate representations which are individually constructed in each party, the proposed method achieves collaborative analysis without revealing the individual data, while the learning models remain distributed over local parties. Numerical experiments indicate that the proposed method achieves higher recognition performance for artificial and real-world problems than individual analysis.

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Footnotes
Literature
1.
Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318. ACM (2016)
2.
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Heidelberg (2006)MATH
3.
Bogdanova, A., Nakai, A., Okada, Y., Imakura, A., Sakurai, T.: Federated learning system without model sharing through integration of dimensional reduced data representations. In: International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with IJCAI 2020 (FL-IJCAI 2020) (2020, accepted)
4.
5.
Cho, H., Wu, D.J., Berger, B.: Secure genome-wide association analysis using multiparty computation. Nat. Biotechnol. 36(6), 547 (2018)CrossRef
6.
7.
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Hum. Genet. 7(2), 179–188 (1936)
8.
Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Stoc, vol. 9, pp. 169–178 (2009)
9.
Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning, pp. 201–210 (2016)
10.
He, X., Niyogi, P.: Locality preserving projections. In: Advances in Neural Information Processing Systems, pp. 153–160 (2004)
11.
Imakura, A., Matsuda, M., Ye, X., Sakurai, T.: Complex moment-based supervised eigenmap for dimensionality reduction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3910–3918 (2019)
12.
13.
Imakura, A., Sakurai, T.: Data collaboration analysis framework using centralization of individual intermediate representations for distributed data sets. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civil Eng. 6, 04020018 (2020)CrossRef
14.
Ito, S., Murota, K.: An algorithm for the generalized eigenvalue problem for nonsquare matrix pencils by minimal perturbation approach. SIAM J. Matrix. Anal. Appl. 37, 409–419 (2016)MathSciNetCrossRef
15.
16.
Ji, Z., Lipton, Z.C., Elkan, C.: Differential privacy and machine learning: a survey and review. arXiv preprint arXiv:​1412.​7584 (2014)
17.
Konečnỳ, J., McMahan, H.B., Ramage, D., Richtarik, P.: Federated optimization: distributed machine learning for on-device intelligence. arXiv preprint arXiv:​1610.​02527 (2016)
18.
Konečnỳ, J., McMahan, H.B., Yu, F.X., Richtarik, P., Suresh, A.T., Bacon, D.: Federated learning: strategies for improving communication efficiency. In: NIPS Workshop on Private Multi-Party Machine Learning (2016). https://​arxiv.​org/​abs/​1610.​05492
19.
20.
Li, X., Chen, M., Nie, F., Wang, Q.: Locality adaptive discriminant analysis. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2201–2207. AAAI Press (2017)
21.
van der Maaten, L., Hinton, G., Visualizing data using t-SNE: J. Machine Learn. Res. 9, 2579–2605 (2008)
22.
McMahan, H.B., Moore, E., Ramage, D., Hampson, S., et al.: Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:​1602.​05629 (2016)
23.
Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space. London Edinburgh Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)CrossRef
24.
Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceeding of IEEE Workshop on Applications of Computer Vision (1994)
25.
Saunders, C., Gammerman, A., Vovk, V.: Ridge regression learning algorithm in dual variables (1998)
26.
Strehl, A., Ghosh, J.: Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)MathSciNetMATH
27.
Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J. Mach. Learn. Res. 8(May), 1027–1061 (2007)MATH
28.
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. Royal Stat. Soci. (Series B) 58, 267–288 (1996)MathSciNetMATH
29.
30.
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2), Article 12 (2019)
31.
Ye, X., Li, H., Imakura, A., Sakurai, T.: Distributed collaborative feature selection based on intermediate representation. In: The 28th International Joint Conference on Artificial Intelligence (IJCAI-19). pp. 4142–4149 (2019)
32.
Metadata
Title
Collaborative Data Analysis: Non-model Sharing-Type Machine Learning for Distributed Data
Authors
Akira Imakura
Xiucai Ye
Tetsuya Sakurai
Copyright Year
2021
Book
Knowledge Management and Acquisition for Intelligent Systems

Print ISBN: 978-3-030-69885-0

Electronic ISBN: 978-3-030-69886-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-69886-7_2

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