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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence / EAAI-20

Private Model Compression via Knowledge Distillation

February 1, 2023

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Authors

Ji Wang

National University of Defense Technology


Weidong Bao

National University of Defense Technology


Lichao Sun

Unversity of Illinois at Chicago


Xiaomin Zhu

National University of Defense Technology


Bokai Cao

Facebook Inc.


Philip S. Yu

University of Illinois at Chicago


DOI:

10.1609/aaai.v33i01.33011190


Abstract:

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices’ capacity. What is worse, app service providers need to collect and utilize a large volume of users’ data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful (9.83,10−6)-differential privacy is guaranteed, the compact model trained by RONA can obtain 20× compression ratio and 19× speed-up with merely 0.97% accuracy loss.

Topics: AAAI

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HOW TO CITE:

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu Private Model Compression via Knowledge Distillation Proceedings of the AAAI Conference on Artificial Intelligence (2019) 1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu Private Model Compression via Knowledge Distillation AAAI 2019, 1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu (2019). Private Model Compression via Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence, 1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu. Private Model Compression via Knowledge Distillation. Proceedings of the AAAI Conference on Artificial Intelligence 2019 p.1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu. 2019. Private Model Compression via Knowledge Distillation. "Proceedings of the AAAI Conference on Artificial Intelligence". 1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu. (2019) "Private Model Compression via Knowledge Distillation", Proceedings of the AAAI Conference on Artificial Intelligence, p.1190-1197

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu, "Private Model Compression via Knowledge Distillation", AAAI, p.1190-1197, 2019.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu. "Private Model Compression via Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence, 2019, p.1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu. "Private Model Compression via Knowledge Distillation". Proceedings of the AAAI Conference on Artificial Intelligence, (2019): 1190-1197.

Ji Wang||Weidong Bao||Lichao Sun||Xiaomin Zhu||Bokai Cao||Philip S. Yu. Private Model Compression via Knowledge Distillation. AAAI[Internet]. 2019[cited 2023]; 1190-1197.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
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