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Federated learning (FL) involves training a model over massive distributed devices, while keeping the training data localized.
Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K · 1937
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Long short-term memory
Hochreiter, S., and Schmidhuber, J · 1997
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Health insurance portability accountability act (hipaa) regulations: effect on medical record research
O’herrin, J. K., Fost, N., and Kudsk, K. A · 2004
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Twitter sentiment classification using distant supervision
Go, A., Bhayani, R., and Huang, L · 2009
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y · 2013
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Introductory lectures on convex optimization: A basic course
Nesterov, Y · 2013
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Adam: A method for stochastic optimization
Kingma, D. P., and Ba, J · 2014
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Deep learning with elastic averaging sgd
Zhang, S., Choromanska, A., and LeCun, Y · 2014
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Giraph unchained: Barrierless asynchronous parallel execution in pregel-like graph processing systems
Han, M., and Daudjee, K · 2015
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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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What the gdpr means for businesses
Tankard, C · 2016
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
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Federated multi-task learning
Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A. S · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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signsgd: Compressed optimisation for non-convex problems
Bernstein, J., Wang, Y.-X., Azizzadenesheli, K., and Anandkumar, A · 2018
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E., and Nocedal, J · 2018
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Leaf: A benchmark for federated settings
Caldas, S., Duddu, S. M. K., Wu, P., Li, T., Konečnỳ, J., McMahan, H. B., Smith, V., and Talwalkar, A · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Hard, A., Rao, K., Mathews, R., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
Cited alongside, same era.
Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., and Kim, S.-L · 2018
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2018
Cited alongside, same era.
Differentially private asynchronous federated learning for mobile edge computing in urban informatics
Lu, Y., Huang, X., Dai, Y., Maharjan, S., and Zhang, Y · 2019
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Communication-efficient federated learning for wireless edge intelligence in iot
Mills, J., Hu, J., and Min, G · 2019
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Client selection for federated learning with heterogeneous resources in mobile edge
Nishio, T., and Yonetani, R · 2019
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An exact quantized decentralized gradient descent algorithm
Reisizadeh, A., Mokhtari, A., Hassani, H., and Pedarsani, R · 2019
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Robust and communication-efficient federated learning from non-iid data
Sattler, F., Wiedemann, S., Müller, K.-R., and Samek, W · 2019
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Sparse binary compression: Towards distributed deep learning with minimal communication
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Lian, X., Zhang, W., Zhang, C., and Liu, J · 2018
Cited alongside, same era.
Wang, J., and Joshi, G · 2018
Cited alongside, same era.
Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
Woodworth, B. E., Wang, J., Smith, A., McMahan, B., and Srebro, N · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Parallel restarted sgd for non-convex optimization with faster convergence and less communication
Yu, H., Yang, S., and Zhu, S · 2018
Cited alongside, same era.
Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Towards taming the resource and data heterogeneity in federated learning
Chai, Z., Fayyaz, H., Fayyaz, Z., Anwar, A., Zhou, Y., Baracaldo, N., Ludwig, H., and Cheng, Y · 2019
Cited alongside, same era.
Sattler, F., Wiedemann, S., Müller, K.-R., and Samek, W · 2019
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Matcha: Speeding up decentralized sgd via matching decomposition sampling
Wang, J., Sahu, A. K., Yang, Z., Joshi, G., and Kar, S · 2019
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Asynchronous federated optimization
Xie, C., Koyejo, S., and Gupta, I · 2019
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Tifl: A tier-based federated learning system
Chai, Z., Ali, A., Zawad, S., Truex, S., Anwar, A., Baracaldo, N., Zhou, Y., Ludwig, H., Yan, F., and Cheng, Y · 2020
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Adaptive asynchronous parallelization of graph algorithms
Fan, W., Lu, P., Yu, W., Xu, J., Yin, Q., Luo, X., Zhou, J., and Jin, R · 2020
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Lessons learned from the chameleon testbed
Keahey, K., Anderson, J., Zhen, Z., Riteau, P., Ruth, P., Stanzione, D., Cevik, M., Colleran, J., Gunawi, H. S., Hammock, C., Mambretti, J., Barnes, A., Halbach, F., Rocha, A., and Stubbs, J · 2020
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V · 2020
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Ibm federated learning: an enterprise framework white paper v0. 1
Ludwig, H., Baracaldo, N., Thomas, G., Zhou, Y., Anwar, A., Rajamoni, S., Ong, Y., Radhakrishnan, J., Verma, A., Sinn, M., et al · 2020
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Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity, 2020
Reisizadeh, A., Tziotis, I., Hassani, H., Mokhtari, A., and Pedarsani, R · 2020
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Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning
Zhang, C., Li, S., Xia, J., Wang, W., Yan, F., and Liu, Y · 2020
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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., and Pedarsani, R · 2031
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