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Split learning of deep neural networks (SplitNN) has provided a promising solution to learning jointly for the mutual interest of a guest and a host, which may come from different backgrounds, holding features partitioned vertically.
Warner, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association (1965)
1965
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE (1998)
1998
Earlier work this paper cites.
Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW (2005)
2005
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science (2014)
2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS (2014)
2014
Earlier work this paper cites.
Harper, A.F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Transactions on Interactive Intelligent Systems (2015)
2015
Earlier work this paper cites.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: ACM SIGSAC CCS (2016)
2016
Earlier work this paper cites.
Hitaj, B., Ateniese, G., Perez-Cruz, F.: Deep models under the gan: information leakage from collaborative deep learning. In: ACM SIGSAC CCS (2017)
2017
Earlier work this paper cites.
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics (2017)
2017
Earlier work this paper cites.
Ganju, K., Wang, Q., Yang, W., Gunter, C.A., Borisov, N.: Property inference attacks on fully connected neural networks using permutation invariant representations. In: ACM SIGSAC CCS (2018)
2018
Earlier work this paper cites.
Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. Journal of Network and Computer Applications (2018)
2018
Earlier work this paper cites.
Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: IEEE S&P (2019)
2019
Earlier work this paper cites.
Molchanov, P., Mallya, A., Tyree, S., Frosio, I., Kautz, J.: Importance estimation for neural network pruning. In: CVPR (2019)
2019
Earlier work this paper cites.
Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In: IEEE S&P (2019)
2019
Cited alongside, same era.
Salem, A., Zhang, Y., Humbert, M., Fritz, M., Backes, M.: Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. In: NDSS (2019)
2019
Cited alongside, same era.
Yu, L., Liu, L., Pu, C., Gursoy, M.E., Truex, S.: Differentially private model publishing for deep learning. In: IEEE S&P (2019)
2019
Cited alongside, same era.
2020
Cited alongside, same era.
Fang, M., Gong, N.Z., Liu, J.: Influence function based data poisoning attacks to top-n recommender systems. In: WWW 2020 (2020)
Luo, X., Wu, Y., Xiao, X., Ooi, B.C.: Feature inference attack on model predictions in vertical federated learning. In: ICDE (2021)
2021
Later among the works it cites.
Mao, Y., Yuan, X., Zhao, X., Zhong, S.: Romoa: Robust model aggregation for the resistance of federated learning to model poisoning attacks. In: ESORICS (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
OpenMined: Syft (2021), https://github.com/OpenMined/PySyft
2021
Later among the works it cites.
Pasquini, D., Ateniese, G., Bernaschi, M.: Unleashing the tiger: Inference attacks on split learning. ACM SIGSAC CCS (2021)
2021
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2020
Cited alongside, same era.
Gao, H., Cai, L., Ji, S.: Adaptive convolutional relus. In: AAAI Conference on Artificial Intelligence (2020)
2020
Cited alongside, same era.
Gao, Y., Kim, M., Abuadbba, S., Kim, Y., Thapa, C., Kim, K., Camtep, S.A., Kim, H., Nepal, S.: End-to-end evaluation of federated learning and split learning for internet of things. In: SRDS (2020)
2020
Cited alongside, same era.
Mao, Y., Zhu, B., Hong, W., Zhu, Z., Zhang, Y., Zhong, S.: Private deep neural network models publishing for machine learning as a service. In: IWQoS (2020)
2020
Cited alongside, same era.
Salem, A., Bhattacharya, A., Backes, M., Fritz, M., Zhang, Y.: Updates-leak: Data set inference and reconstruction attacks in online learning. In: USENIX Security Symposium (2020)
2020
Cited alongside, same era.
Tolpegin, V., Truex, S., Gursoy, M.E., Liu, L.: Data poisoning attacks against federated learning systems. In: ESORICS (2020)
2020
Cited alongside, same era.
Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., Liu, Y.: { \{ BatchCrypt } \} : Efficient homomorphic encryption for { \{ Cross-Silo } \} federated learning. In: 2020 USENIX annual technical conference (USENIX ATC 20) (2020)
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Later among the works it cites.
Sun, L., Qian, J., Chen, X.: LDP-FL: practical private aggregation in federated learning with local differential privacy. In: IJCAI (2021)
2021
Later among the works it cites.
Webank: Fate (2021), https://github.com/FederatedAI/FATE
2021
Later among the works it cites.
Erdogan, E., Kupcu, A., Cicek, A.E.: Unsplit: Data-oblivious model inversion, model stealing, and label inference attacks against split learning. In: Proceedings of the 21st Workshop on Privacy in the Electronic Society, WPES2022 (2022)
2022
Later among the works it cites.
Fu, C., Zhang, X., Ji, S., Chen, J., Wu, J., Guo, S., Zhou, J., Liu, A.X., Wang, T.: Label inference attacks against vertical federated learning. In: USENIX Security 22 (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Li, J., Rakin, A.S., Chen, X., He, Z., Fan, D., Chakrabarti, C.: Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning. In: CVPR (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
Zheng, Y., Lai, S., Liu, Y., Yuan, X., Yi, X., Wang, C.: Aggregation service for federated learning: An efficient, secure, and more resilient realization. IEEE Transactions on Dependable and Secure Computing (2022)
2022
Later among the works it cites.