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Federated learning was proposed with an intriguing vision of achieving collaborative machine learning among numerous clients without uploading their private data to a cloud server.
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2015
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H. Cheng, L. Koc, J. Harmsen, T. Shaked, T. Chandra, H. Aradhye, G. Anderson, G. Corrado, W. Chai, M. Ispir, R. Anil, Z. Haque, L. Hong, V. Jain, X. Liu, and H. Shah, “Wide & deep learning for recommender systems,” in Proc. of the 1st Workshop on Deep Learning for Recommender Systems (DLRS) , 2016, pp. 7–10
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2018
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2018
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2018
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2018
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L. Melis, C. Song, E. D. Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning,” in Proc. of S&P , 2019, pp. 497–512
2019
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G. Zhou, N. Mou, Y. Fan, Q. Pi, W. Bian, C. Zhou, X. Zhu, and K. Gai, “Deep interest evolution network for click-through rate prediction,” in Proc. of AAAI , 2019
2019
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A. A. Ginart, M. Guan, G. Valiant, and J. Zou, “Making ai forget you: Data deletion in machine learning,” in Proc. of NeurIPS , 2019
2019
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Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 10, no. 2, pp. 12:1–12:19, 2019
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H. Yu, S. Yang, and S. Zhu, “Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning,” in Proc. of AAAI , 2019, pp. 5693–5700
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M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in Proc. of ICML , 2019, pp. 4615–4625
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2019
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2019
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“Tensorflow federated: Machine learning on decentralized data,” https://www.tensorflow.org/federated
2019
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L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” in Proc. of NeurIPS , 2019
2019
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Ú. Erlingsson, V. Feldman, I. Mironov, A. Raghunathan, K. Talwar, and A. Thakurta, “Amplification by shuffling: From local to central differential privacy via anonymity,” in Proc. of SODA , 2019, pp. 2468–2479
2019
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T. Wang, B. Ding, J. Zhou, C. Hong, Z. Huang, N. Li, and S. Jha, “Answering multi-dimensional analytical queries under local differential privacy,” in Proc. of SIGMOD , 2019, pp. 159–176
2019
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V. Kolesnikov, M. Rosulek, N. Trieu, and X. Wang, “Scalable private set union from symmetric-key techniques,” IACR Cryptology ePrint Archive, Report 2019/776, 2019, https://eprint.iacr.org/2019/776
2019
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