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The increasing concerns about data privacy and security drive an emerging field of studying privacy-preserving machine learning from isolated data sources, i.e., federated learning.
Evaluation specification of artificial intelligence algorithm in financial application
The People’s Bank of China · 1912
Earlier work this paper cites.
Credit scoring and the equal credit opportunity act
Hsia, D. C · 1978
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Friedman, J. H · 2001
Earlier work this paper cites.
Privacy preserving decision tree learning over vertically partitioned data
Fang, W. and Yang, B · 2008
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Ensemble learning
Zhou, Z.-H · 2009
Earlier work this paper cites.
Fully homomorphic encryption over the integers
Van Dijk, M., Gentry, C., Halevi, S., and Vaikuntanathan, V · 2010
Earlier work this paper cites.
Give me some credit
Give me some credit · 2011
Earlier work this paper cites.
Consensus-based distributed optimization: Practical issues and applications in large-scale machine learning
Tsianos, K. I., Lawlor, S., and Rabbat, M. G · 2012
Earlier work this paper cites.
OJ L 119 , pp. 1–88, 2016
Regulation (EU) 2016/679 of the European Parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (General Data Protection Regulation) · 2016
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Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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default of credit card clients data set
UCI Machine Learning Repository · 2017
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SecureGBM: Secure multi-party gradient boosting
Feng, Z., Xiong, H., Song, C., Yang, S., Zhao, B., Wang, L., Chen, Z., Yang, S., Liu, L., and Huan, J · 2019
Cited alongside, same era.
A quasi-newton method based vertical federated learning framework for logistic regression
Yang, K., Fan, T., Chen, T., Shi, Y., and Yang, Q · 2019
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Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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Federated doubly stochastic kernel learning for vertically partitioned data
Gu, B., Dang, Z., Li, X., and Huang, H · 2020
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FedBoost: A communication-efficient algorithm for federated learning
Hamer, J., Mohri, M., and Suresh, A. T · 2020
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Practical federated gradient boosting decision trees
Li, Q., Wen, Z., and He, B · 2020
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Adaptive histogram-based gradient boosted trees for federated learning
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Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2019
Cited alongside, same era.
Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T · 2019
Cited alongside, same era.
A secure federated transfer learning framework
Liu, Y., Kang, Y., Xing, C., Chen, T., and Yang, Q
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Federated forest
Liu, Y., Liu, Y., Liu, Z., Liang, Y., Meng, C., Zhang, J., and Zheng, Y
Cited in the paper.
Cryptosystems based on composite residuosity
Paillier, P
Cited in the paper.
Public-key cryptosystems based on composite degree residuosity classes
Paillier, P
Cited in the paper.
Ong, Y. J., Zhou, Y., Baracaldo, N., and Ludwig, H · 2020
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SecureBoost: A lossless federated learning framework
Cheng, Kewei and Fan, Tao and Jin, Yilun and Liu, Yang and Chen, Tianjian and Papadopoulos, Dimitrios and Yang, Qiang · 2021
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