Privacy-Preserving Federated Learning via Disentanglement

Author:

Zhou Wenjie1ORCID,Li Piji1ORCID,Han Zhaoyang1ORCID,Lu Xiaozhen1ORCID,Li Juan1ORCID,Ren Zhaochun2ORCID,Liu Zhe1ORCID

Affiliation:

1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. Shandong University, Jinan, China

Funder

the Scientific Research Starting Foundation of Nanjing University of Aeronautics and Astronautics

the National Natural Science Foundation of China

the CCF-Zhipu AI Large Model Fund

the High Performance Computing Platform of Nanjing University of Aeronautics and Astronautics

the CCF-Tencent Open Research Fund

Publisher

ACM

Reference37 articles.

1. X. An , J. Deng , J. Guo , Z. Feng , X. Zhu , J. Yang , and T. Liu , ?Killing two birds with one stone: Efficient and robust training of face recognition cnns by partial fc," in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022 , pp. 4032 -- 4041 . X. An, J. Deng, J. Guo, Z. Feng, X. Zhu, J. Yang, and T. Liu, ?Killing two birds with one stone: Efficient and robust training of face recognition cnns by partial fc," in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 4032--4041.

2. Y. Luo , M. Xu , and D. Xiong , ?CogTaskonomy: Cognitively inspired task taxonomy is beneficial to transfer learning in NLP," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics , 2022 , pp. 904 -- 920 . Y. Luo, M. Xu, and D. Xiong, ?CogTaskonomy: Cognitively inspired task taxonomy is beneficial to transfer learning in NLP," in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 904--920.

3. B. McMahan , E. Moore , D. Ramage , S. Hampson , and B. A. y Arcas , ?Communication-efficient learning of deep networks from decentralized data," in Artificial intelligence and statistics . PMLR , 2017 , pp. 1273 -- 1282 . B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, ?Communication-efficient learning of deep networks from decentralized data," in Artificial intelligence and statistics. PMLR, 2017, pp. 1273--1282.

4. B. McMahan , E. Moore , D. Ramage , S. Hampson , and B. A. y. Arcas , ?Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics , vol. 54 , 2017 , pp. 1273 -- 1282 . B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y. Arcas, ?Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol. 54, 2017, pp. 1273--1282.

5. T. Li , A. K. Sahu , M. Zaheer , M. Sanjabi , A. Talwalkar , and V. Smith , ?Federated optimization in heterogeneous networks," Proceedings of Machine learning and systems , vol. 2 , pp. 429 -- 450 , 2020 . T. Li, A. K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, and V. Smith, ?Federated optimization in heterogeneous networks," Proceedings of Machine learning and systems, vol. 2, pp. 429--450, 2020.

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