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Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
A universal modular ACTOR formalism for artificial intelligence
Hewitt, C., Bishop, P. B., and Steiger, R · 1973
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MapReduce: Simplified data processing on large clusters
Dean, J. and Ghemawat, S · 2008
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Large scale distributed deep networks
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q. V., Mao, M., Ranzato, M., Senior, A., Tucker, P., Yang, K., and Ng, A. Y · 2012
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Distributed graphlab: A framework for machine learning and data mining in the cloud
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., and Hellerstein, J. M · 2012
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Scaling distributed machine learning with the parameter server
Li, M., Andersen, D. G., Park, J. W., Smola, A. J., Ahmed, A., Josifovski, V., Long, J., Shekita, E. J., and Su, B.-Y · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 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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Accurate, large minibatch SGD: Training imagenet in 1 hour
Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., and He, K · 2017
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2017
Cited alongside, same era.
Federated learning: Collaborative machine learning without centralized training data, April 2017
McMahan, H. B. and Ramage, D · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
Cited alongside, same era.
Under the hood of the pixel 2: How ai is supercharging hardware, 2018
ai.google · 2018
Cited alongside, same era.
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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Efficient decentralized deep learning by dynamic model averaging
Kamp, M., Adilova, L., Sicking, J., Hüger, F., Schlicht, P., Wirtz, T., and Wrobel, S · 2018
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Learning differentially private recurrent language models
McMahan, H. B., Ramage, D., Talwar, K., and Zhang, L · 2018
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Client selection for federated learning with heterogeneous resources in mobile edge
Nishio, T. and Yonetani, R · 2018
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Differentially-private “draw and discard” machine learning
Pihur, V., Korolova, A., Liu, F., Sankuratripati, S., Yung, M., Huang, D., and Zeng, R · 2018
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Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., and Shmatikov, V · 2018
Cited alongside, same era.
Federated learning of predictive models from federated electronic health records
Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., and Shi, W · 2018
Cited alongside, same era.
Expanding the reach of federated learning by reducing client resource requirements
Caldas, S., Konecný, J., McMahan, H. B., and Talwalkar, A · 2018
Cited alongside, same era.
SafetyNet Attestation API
Android Documentation
Cited in the paper.
Federated optimization: Distributed machine learning for on-device intelligence
Konečný, J., McMahan, H. B., Ramage, D., and Richtárik, P
Cited in the paper.
Federated learning: Strategies for improving communication efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D
Cited in the paper.
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Federated learning for ultra-reliable low-latency v2v communications
Samarakoon, S., Bennis, M., Saad, W., and Debbah, M · 2018
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Don’t decay the learning rate, increase the batch size
Smith, S., jan Kindermans, P., Ying, C., and Le, Q. V · 2018
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Applied federated learning: Improving google keyboard query suggestions
Yang, T., Andrew, G., Eichner, H., Sun, H., Li, W., Kong, N., Ramage, D., and Beaufays, F · 2018
Later among the works it cites.