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In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data localized.
The space complexity of approximating the frequency moments
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Secure multi-party computation problems and their applications: A review and open problems
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Finding frequent items in data streams
Charikar, M., Chen, K., and Farach-Colton, M · 2002
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An elementary proof of a theorem of johnson and lindenstrauss
Dasgupta, S., and Gupta, A · 2003
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Reversible sketches for efficient and accurate change detection over network data streams
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An Improved Data Stream Summary: The Count-min Sketch and Its Applications
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Differential privacy
Dwork, C · 2006
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Calibrating noise to sensitivity in private data analysis
Dwork, C., McSherry, F., Nissim, K., and Smith, A · 2006
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On privacy-preservation of text and sparse binary data with sketches
Aggarwal, C. C., and Yu, P. S · 2007
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An empirical evaluation of entropy-based traffic anomaly detection
Nychis, G., Sekar, V., Andersen, D. G., Kim, H., and Zhang, H · 2008
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Security technology building a secure system using trustzone technology (white paper)
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A near-optimal algorithm for estimating the entropy of a stream
Chakrabarti, A., Cormode, G., and Mcgregor, A · 2010
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Fully homomorphic encryption from ring-lwe and security for key dependent messages
Brakerski, Z., and Vaikuntanathan, V · 2011
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Communication-efficient distributed optimization using an approximate newton-type method
Shamir, O., Srebro, N., and Zhang, T · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Fredrikson, M., Jha, S., and Ristenpart, T · 2015
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Distributed face recognition using collaborative judgement aggregation in a swarm of tiny wireless sensor nodes
Gaynor, P., and Coore, D · 2015
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Emnist: an extension of mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and van Schaik, A · 2017
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Differentially private federated learning: A client level perspective
Geyer, R. C., Klein, T., and Nabi, M · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and Arcas, B. A. y · 2017
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Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
Agarwal, N., Suresh, A. T., Yu, F. X. X., Kumar, S., and McMahan, B · 2018
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Melis, L., Danezis, G., and De Cristofaro, E · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M. K., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X · 2016
Cited alongside, same era.
Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
Cited alongside, same era.
The Probabilistic Method
Alon, N., and Spencer, J. H · 2016
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Intel sgx explained
Costan, V., and Devadas, S · 2016
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One sketch to rule them all: Rethinking network flow monitoring with univmon
Liu, Z., Manousis, A., Vorsanger, G., Sekar, V., and Braverman, V · 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
Cited alongside, same era.
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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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Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
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Sketchml: Accelerating distributed machine learning with data sketches
Jiang, J., Fu, F., Yang, T., and Cui, B · 2018
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Protection against reconstruction and its applications in private federated learning
Bhowmick, A., Freudiger, J. D. J., Kapoor, G., and Rogers, R · 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., Overveldt, T. V., Petrou, D., Ramage, D., and Roselande, J · 2019
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Privacy for Free: Communication-Efficient Learning with Differential Privacy using Sketches
Li, T., Liu, Z., Sekar, V., and Smith, V · 2019
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2019
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