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Federated Learning enables visual models to be trained on-device, bringing advantages for user privacy (data need never leave the device), but challenges in terms of data diversity and quality.
1902
Earlier work this paper cites.
Kahn, H., Marshall, A.W.: Methods of reducing sample size in Monte Carlo computations. Journal of the Operations Research Society of America 1
1953
Earlier work this paper cites.
Hesterberg, T.: Weighted average importance sampling and defensive mixture distributions. Technometrics 37
1995
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86
1998
Earlier work this paper cites.
Saerens, M., Latinne, P., Decaestecker, C.: Adjusting the outputs of a classifier to new a priori probabilities: a simple procedure. Neural computation 14
2002
Earlier work this paper cites.
Hays, J., Efros, A.A.: IM2GPS: Estimating geographic information from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1–8. IEEE (2008)
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 248–255. IEEE (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Zhang, K., Schölkopf, B., Muandet, K., Wang, Z.: Domain adaptation under target and conditional shift. In: Proceedings of the International Conference on Machine Learning (ICML). pp. 819–827 (2013)
2013
Earlier work this paper cites.
Doersch, C., Singh, S., Gupta, A., Sivic, J., Efros, A.A.: What makes Paris look like Paris? Communications of the ACM 58
2015
Earlier work this paper cites.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (2015)
2015
Earlier work this paper cites.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1–9 (2015)
2015
Earlier work this paper cites.
Weyand, T., Kostrikov, I., Philbin, J.: Planet-photo geolocation with convolutional neural networks. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 37–55. Springer (2016)
2016
Earlier work this paper cites.
Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: Emnist: Extending mnist to handwritten letters. In: 2017 International Joint Conference on Neural Networks (IJCNN). pp. 2921–2926. IEEE (2017)
2017
Cited alongside, same era.
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics. pp. 1273–1282 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Google: TensorFlow Federated (2019), https://www.tensorflow.org/federated
2019
Later among the works it cites.
Google: TensorFlow Federated Datasets (2019), https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
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McMahan, H.B., Ramage, D., Talwar, K., Zhang, L.: Learning differentially private recurrent language models. In: International Conference on Learning Representations (ICLR) (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4510–4520 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The iNaturalist species classification and detection dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8769–8778 (2018)
2018
Cited alongside, same era.
Wu, Y., He, K.: Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 3–19 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
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2019
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2019
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2019
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2019
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2019
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Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., Khazaeni, Y.: Bayesian nonparametric federated learning of neural networks. In: Proceedings of the International Conference on Machine Learning (ICML). pp. 7252–7261 (2019)
2019
Later among the works it cites.
Weyand, T., Araujo, A., Cao, B., Sim, J.: Google Landmarks Dataset v2 - a large-scale benchmark for instance-level recognition and retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)
2020
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