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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of AISTATS. 1273--1282."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01210"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21465"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"e_1_3_2_2_32_1","volume-title":"Proceedings of ICLR.","author":"Ravi Sachin","year":"2017","unstructured":"Sachin Ravi and Hugo Larochelle . 2017 . Optimization as a model for few-shot learning . In Proceedings of ICLR. Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In Proceedings of ICLR."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari Holger R Roth Shadi Albarqouni Spyridon Bakas Mathieu N Galtier Bennett A Landman Klaus Maier-Hein etal 2020. The future of digital health with federated learning. NPJ digital medicine 3 1 (2020) 1--7.  Nicola Rieke Jonny Hancox Wenqi Li Fausto Milletari Holger R Roth Shadi Albarqouni Spyridon Bakas Mathieu N Galtier Bennett A Landman Klaus Maier-Hein et al. 2020. The future of digital health with federated learning. NPJ digital medicine 3 1 (2020) 1--7.","DOI":"10.1038\/s41746-020-00323-1"},{"key":"e_1_3_2_2_34_1","volume-title":"Advances in NeurIPS","volume":"31","author":"Shafahi Ali","year":"2018","unstructured":"Ali Shafahi , W Ronny Huang , Mahyar Najibi , Octavian Suciu , Christoph Studer , Tudor Dumitras , and Tom Goldstein . 2018 . Poison frogs! targeted clean-label poisoning attacks on neural networks . 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On the geometry of generalization and memorization in deep neural networks. In Proceedings of ICLR."},{"key":"e_1_3_2_2_39_1","first-page":"12613","article-title":"Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective","volume":"34","author":"Sun Jingwei","year":"2021","unstructured":"Jingwei Sun , Ang Li , Louis DiValentin , Amin Hassanzadeh , Yiran Chen , and Hai Li . 2021 . Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective . Advances in NeurIPS 34 (2021), 12613 -- 12624 . Jingwei Sun, Ang Li, Louis DiValentin, Amin Hassanzadeh, Yiran Chen, and Hai Li. 2021. Fl-wbc: Enhancing robustness against model poisoning attacks in federated learning from a client perspective. Advances in NeurIPS 34 (2021), 12613--12624.","journal-title":"Advances in NeurIPS"},{"key":"e_1_3_2_2_40_1","volume-title":"Ananda Theertha Suresh, and H Brendan McMahan","author":"Sun Ziteng","year":"2019","unstructured":"Ziteng Sun , Peter Kairouz , Ananda Theertha Suresh, and H Brendan McMahan . 2019 . Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019). Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, and H Brendan McMahan. 2019. Can you really backdoor federated learning? arXiv preprint arXiv:1911.07963 (2019)."},{"key":"e_1_3_2_2_41_1","volume-title":"Deepesh Data, et al","author":"Wang Jianyu","year":"2021","unstructured":"Jianyu Wang , Zachary Charles , Zheng Xu , Gauri Joshi , H Brendan McMahan , Maruan Al-Shedivat , Galen Andrew , Salman Avestimehr , Katharine Daly , Deepesh Data, et al . 2021 . A field guide to federated optimization. arXiv preprint arXiv:2107.06917 (2021). Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, et al. 2021. A field guide to federated optimization. arXiv preprint arXiv:2107.06917 (2021)."},{"key":"e_1_3_2_2_42_1","volume-title":"Communication-efficient federated learning via knowledge distillation. Nature communications 13, 1","author":"Wu Chuhan","year":"2022","unstructured":"Chuhan Wu , Fangzhao Wu , Lingjuan Lyu , Yongfeng Huang , and Xing Xie . 2022. Communication-efficient federated learning via knowledge distillation. Nature communications 13, 1 ( 2022 ), 1--8. Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, and Xing Xie. 2022. Communication-efficient federated learning via knowledge distillation. Nature communications 13, 1 (2022), 1--8."},{"key":"e_1_3_2_2_43_1","volume-title":"FedCTR: Federated Native Ad CTR Prediction with Cross Platform User Behavior Data. 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Adversarial label flips attack on support vector machines. In Proceedings of ECAI. IOS Press, 870--875."},{"key":"e_1_3_2_2_46_1","volume-title":"Generalized byzantine- tolerant sgd. arXiv preprint arXiv:1802.10116","author":"Xie Cong","year":"2018","unstructured":"Cong Xie , Oluwasanmi Koyejo , and Indranil Gupta . 2018. Generalized byzantine- tolerant sgd. arXiv preprint arXiv:1802.10116 ( 2018 ). Cong Xie, Oluwasanmi Koyejo, and Indranil Gupta. 2018. Generalized byzantine- tolerant sgd. arXiv preprint arXiv:1802.10116 (2018)."},{"key":"e_1_3_2_2_47_1","volume-title":"Proceedings of ICML. 5650--5659","author":"Yin Dong","year":"2018","unstructured":"Dong Yin , Yudong Chen , Ramchandran Kannan , and Peter Bartlett . 2018 . Byzantine-robust distributed learning: Towards optimal statistical rates . In Proceedings of ICML. 5650--5659 . Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett. 2018. Byzantine-robust distributed learning: Towards optimal statistical rates. 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