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To preserve user privacy while enabling mobile intelligence, techniques have been proposed to train deep neural networks on decentralized data.
Secure multiparty computation for privacy preserving data mining
Lindell, Y · 2005
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Towards service composition based on mashup
Liu, X., Hui, Y., Sun, W., and Liang, H · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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imashup: a mashup-based framework for service composition
Liu, X., Huang, G., Zhao, Q., Mei, H., and Blake, M. B · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al · 2016
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Practical secure aggregation for privacy-preserving machine learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., and Seth, K · 2017
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Smash: one-shot model architecture search through hypernetworks
Brock, A., Lim, T., Ritchie, J. M., and Weston, N · 2017
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Google vizier: A service for black-box optimization
Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., and Sculley, D · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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ATM: A distributed, collaborative, scalable system for automated machine learning
Swearingen, T., Drevo, W., Cyphers, B., Cuesta-Infante, A., Ross, A., and Veeramachaneni, K · 2017
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Designing energy-efficient convolutional neural networks using energy-aware pruning
Yang, T.-J., Chen, Y.-H., and Sze, V · 2017
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Neural architecture search with reinforcement learning
Zoph, B., and Le, Q. V · 2017
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Understanding and simplifying one-shot architecture search
Bender, G., Kindermans, P., Zoph, B., Vasudevan, V., and Le, Q. V · 2018
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Path-level network transformation for efficient architecture search
Cai, H., Yang, J., Zhang, W., Han, S., and Yu, Y · 2018
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Proxylessnas: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2018
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Leaf: A benchmark for federated settings
Caldas, S., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Gordon, A., Eban, E., Nachum, O., Chen, B., Wu, H., Yang, T.-J., and Choi, E · 2018
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https://gdpr-info.eu/ , 2019
General data protection regulation (gdpr) · 2019
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Towards federated learning at scale: System design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konecny, J., Mazzocchi, S., McMahan, H. B., et al · 2019
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Advances and open problems in federated learning
Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al · 2019
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Federated learning for keyword spotting
Leroy, D., Coucke, A., Lavril, T., Gisselbrecht, T., and Dureau, J · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
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Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
Cited alongside, same era.
Progressive neural architecture search
Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., and Murphy, K · 2018
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Secure federated transfer learning
Liu, Y., Chen, T., and Yang, Q · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Efficient neural architecture search via parameter sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018
Cited alongside, same era.
Not just privacy: Improving performance of private deep learning in mobile cloud
Wang, J., Zhang, J., Bao, W., Zhu, X., Cao, B., and Yu, P. S · 2018
Cited alongside, same era.
Deepcache: principled cache for mobile deep vision
Xu, M., Zhu, M., Liu, Y., Lin, F. X., and Liu, X · 2018
Cited alongside, same era.
Netadapt: Platform-aware neural network adaptation for mobile applications
Yang, T.-J., Howard, A., Chen, B., Zhang, X., Go, A., Sandler, M., Sze, V., and Adam, H · 2018
Cited alongside, same era.
Wu, B., Dai, X., Zhang, P., Wang, Y., Sun, F., Wu, Y., Tian, Y., Vajda, P., Jia, Y., and Keutzer, K · 2019
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A first look at deep learning apps on smartphones
Xu, M., Liu, J., Liu, Y., Lin, F. X., Liu, Y., and Liu, X · 2019
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Katib: A distributed general automl platform on kubernetes
Zhou, J., Velichkevich, A., Prosvirov, K., Garg, A., Oshima, Y., and Dutta, D · 2019
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Fednas: Federated deep learning via neural architecture search
He, C., Annavaram, M., and Avestimehr, S · 2020
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Towards ubiquitous learning: A first measurement of on-device training performance
Cai, D., Wang, Q., Liu, Y., Liu, Y., Wang, S., and Xu, M · 2021
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Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data
Yang, C., Wang, Q., Xu, M., Chen, Z., Bian, K., Liu, Y., and Liu, X · 2021
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Autofednlp: An efficient fednlp framework
Cai, D., Wu, Y., Wang, S., Lin, F. X., and Xu, M · 2022
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Melon: Breaking the memory wall for resource-efficient on-device machine learning
Wang, Q., Xu, M., Jin, C., Dong, X., Yuan, J., Jin, X., Huang, G., Liu, Y., and Liu, X · 2022
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Mandheling: Mixed-precision on-device dnn training with dsp offloading
Xu, D., Xu, M., Wang, Q., Wang, S., Ma, Y., Huang, K., Huang, G., Jin, X., and Liu, X · 2022
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A comprehensive benchmark of deep learning libraries on mobile devices
Zhang, Q., Li, X., Che, X., Ma, X., Zhou, A., Xu, M., Wang, S., Ma, Y., and Liu, X · 2022
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