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As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed to preserve the data privacy of the participating organizations.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H. B., et al · 1902
Earlier work this paper cites.
Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečnỳ, J., Mazzocchi, S., McMahan, H. B., et al · 1902
Earlier work this paper cites.
On data banks and privacy homomorphisms
Rivest, R. L., Adleman, L., Dertouzos, M. L., et al · 1978
Earlier work this paper cites.
Public-key cryptosystems based on composite degree residuosity classes
Paillier, P · 1999
Earlier work this paper cites.
Crafting papers on machine learning
Langley, P · 2000
Earlier work this paper cites.
Privacy-preserving decision trees over vertically partitioned data
Vaidya, J. and Clifton, C · 2005
Earlier work this paper cites.
Privacy-preserving decision trees over vertically partitioned data
Vaidya, J., Clifton, C., Kantarcioglu, M., and Patterson, A. S · 2008
Earlier work this paper cites.
A fully homomorphic encryption scheme , volume 20
Gentry, C. et al · 2009
Earlier work this paper cites.
Research on diffie-hellman key exchange protocol
Li, N · 2010
Earlier work this paper cites.
LIBSVM: A library for support vector machines
Chang, C.-C. and Lin, C.-J · 2011
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
Earlier work this paper cites.
Blockchain for iot security and privacy: The case study of a smart home
Dorri, A., Kanhere, S. S., Jurdak, R., and Gauravaram, P · 2017
Cited alongside, same era.
Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., and Thorne, B · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Secureboost: A lossless federated learning framework
Cheng, K., Fan, T., Jin, Y., Liu, Y., Chen, T., and Yang, Q · 2019
Industrial federated learning–requirements and system design
Hiessl, T., Schall, D., Kemnitz, J., and Schulte, S · 2020
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Federated learning for open banking
Long, G., Tan, Y., Jiang, J., and Zhang, C · 2020
Later among the works it cites.
Federated learning with blockchain for autonomous vehicles: Analysis and design challenges
Pokhrel, S. R. and Choi, J · 2020
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al · 2020
Later among the works it cites.
Federated recommendation systems
Yang, L., Tan, B., Zheng, V. W., Chen, K., and Yang, Q · 2020
Later among the works it cites.
Federated learning for predicting clinical outcomes in patients with covid-19
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Cited alongside, same era.
Advances and open problems in federated learning
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.
Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
Cited alongside, same era.
Flower: A friendly federated learning research framework
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., de Gusmão, P. P., and Lane, N. D · 2020
Cited alongside, same era.
Federated doubly stochastic kernel learning for vertically partitioned data
Gu, B., Dang, Z., Li, X., and Huang, H · 2020
Cited alongside, same era.
Fedml: A research library and benchmark for federated machine learning
He, C., Li, S., So, J., Zeng, X., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., et al · 2020
Cited alongside, same era.
A secure federated transfer learning framework
Liu, Y., Kang, Y., Xing, C., Chen, T., and Yang, Q
Cited in the paper.
Federated forest
Liu, Y., Liu, Y., Liu, Z., Liang, Y., Meng, C., Zhang, J., and Zheng, Y
Cited in the paper.
Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al · 2021
Later among the works it cites.
Secure and efficient multiparty private set intersection cardinality
Debnath, S. K., Stǎnicǎ, P., Kundu, N., and Choudhury, T · 2021
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Byzshield: An efficient and robust system for distributed training
Konstantinidis, K. and Ramamoorthy, A · 2021
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Stochastic proximal auc maximization
Lei, Y. and Ying, Y · 2021
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Fedlearn-algo: A flexible open-source privacy-preserving machine learning platform
Liu, B., Tan, C., Wang, J., Zeng, T., Shan, H., Yao, H., Heng, H., Dai, P., Bo, L., and Chen, Y · 2021
Later among the works it cites.