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In a vertical federated learning (VFL) system consisting of a central server and many distributed clients, the training data are vertically partitioned such that different features are privately stored on different clients.
Yang, S., Ren, B., Zhou, X., and Liu, L · 1911
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Yang, S., Ren, B., Zhou, X., and Liu, L · 1911
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Multi-participant multi-class vertical federated learning
Feng, S. and Yu, H · 2001
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VAFL: a method of vertical asynchronous federated learning
Chen, T., Jin, X., Sun, Y., and Yin, W · 2007
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Ceballos, I., Sharma, V., Mugica, E., Singh, A., Roman, A., Vepakomma, P., and Raskar, R · 2008
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The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients
Yeh, I.-C. and Lien, C.-h · 2009
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Modern computer algebra
Von Zur Gathen, J. and Gerhard, J · 2013
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On the computational efficiency of training neural networks
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Hardy, S., Henecka, W., Ivey-Law, H., Nock, R., Patrini, G., Smith, G., and Thorne, B · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Privacy-preserving patient similarity learning in a federated environment: development and analysis
Lee, J., Sun, J., Wang, F., Wang, S., Jun, C.-H., Jiang, X., et al · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Vepakomma, P., Gupta, O., Swedish, T., and Raskar, R · 2018
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Coded federated learning
Dhakal, S., Prakash, S., Yona, Y., Talwar, S., and Himayat, N · 2019
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Fdml: A collaborative machine learning framework for distributed features
Hu, Y., Niu, D., Yang, J., and Zhou, S · 2019
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A comparative analysis of speech signal processing algorithms for parkinson’s disease classification and the use of the tunable q-factor wavelet transform
Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tutuncu, M., Aydin, T., Isenkul, M. E., and Apaydin, H · 2019
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A hybrid approach to privacy-preserving federated learning
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R., and Zhou, Y · 2019
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Asynchronous federated optimization
Xie, C., Koyejo, S., and Gupta, I · 2019
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Lagrange coded computing: Optimal design for resiliency, security, and privacy
Yu, Q., Li, S., Raviv, N., Kalan, S. M. M., Soltanolkotabi, M., and Avestimehr, S. A · 2019
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Secure single-server aggregation with (poly) logarithmic overhead
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Chai, D., Wang, L., Chen, K., and Yang, Q · 2020
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Communication-computation efficient secure aggregation for federated learning
Choi, B., Sohn, J.-y., Han, D.-J., and Moon, J · 2020
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Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression
Feature inference attack on model predictions in vertical federated learning
Luo, X., Wu, Y., Xiao, X., and Ooi, B. C · 2021
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Sageflow: Robust federated learning against both stragglers and adversaries
Park, J., Han, D.-J., Choi, M., and Moon, J · 2021
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So, J., Güler, B., and Avestimehr, A. S · 2021
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Li, M., Chen, Y., Wang, Y., and Pan, Y · 2020
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Coded computing for low-latency federated learning over wireless edge networks
Prakash, S., Dhakal, S., Akdeniz, M. R., Yona, Y., Talwar, S., Avestimehr, S., and Himayat, N · 2020
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Gcn meets gpu: Decoupling “when to sample”from “how to sample”
Ramezani, M., Cong, W., Mahdavi, M., Sivasubramaniam, A., and Kandemir, M · 2020
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Ldp-fed: Federated learning with local differential privacy
Truex, S., Liu, L., Chow, K.-H., Gursoy, M. E., and Wei, W · 2020
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van Dijk, M., Nguyen, N. V., Nguyen, T. N., Nguyen, L. M., Tran-Dinh, Q., and Nguyen, P. H · 2020
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Hybrid differentially private federated learning on vertically partitioned data
Wang, C., Liang, J., Huang, M., Bai, B., Bai, K., and Li, H · 2020
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Federated learning with differential privacy: Algorithms and performance analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q., and Poor, H. V · 2020
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Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., and Gao, Y · 2021
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Secure forward aggregation for vertical federated neural networks
Cai, S., Chai, D., Yang, L., Zhang, J., Jin, Y., Wang, L., Guo, K., and Chen, K · 2022
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Flexible vertical federated learning with heterogeneous parties
Castiglia, T., Wang, S., and Patterson, S · 2022
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Label inference attacks against vertical federated learning
Fu, C., Zhang, X., Ji, S., Chen, J., Wu, J., Guo, S., Zhou, J., Liu, A. X., and Wang, T · 2022
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Papaya: Practical, private, and scalable federated learning
Huba, D., Nguyen, J., Malik, K., Zhu, R., Rabbat, M., Yousefpour, A., Wu, C.-J., Zhan, H., Ustinov, P., Srinivas, H., et al · 2022
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Swiftagg: Communication-efficient and dropout-resistant secure aggregation for federated learning with worst-case security guarantees
Jahani-Nezhad, T., Maddah-Ali, M. A., Li, S., and Caire, G · 2022
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Caltech 101, April 2022
Li, F.-F., Andreeto, M., Ranzato, M., and Perona, P · 2022
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Federated learning with buffered asynchronous aggregation
Nguyen, J., Malik, K., Zhan, H., Yousefpour, A., Rabbat, M., Malek, M., and Huba, D · 2022
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Reisizadeh, A., Tziotis, I., Hassani, H., Mokhtari, A., and Pedarsani, R · 2022
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Dres-fl: Dropout-resilient secure federated learning for non-iid clients via secret data sharing
Shao, J., Sun, Y., Li, S., and Zhang, J · 2022
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Efficient asynchronous multi-participant vertical federated learning
Shi, H., Xu, Y., Jiang, Y., Yu, H., and Cui, L · 2022
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Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning
So, J., He, C., Yang, C.-S., Li, S., Yu, Q., E Ali, R., Guler, B., and Avestimehr, S · 2022
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Splitfed: When federated learning meets split learning
Thapa, C., Arachchige, P. C. M., Camtepe, S., and Sun, L · 2022
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Vertical federated learning: Challenges, methodologies and experiments
Wei, K., Li, J., Ma, C., Ding, M., Wei, S., Wu, F., Chen, G., and Ranbaduge, T · 2022
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Stochastic coded federated learning with convergence and privacy guarantees
Sun, Y., Shao, J., Li, S., Mao, Y., and Zhang, J · 2033
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