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Federated Learning (FL) enables collaborations among clients for train machine learning models while protecting their data privacy.
Connection-level analysis and modeling of network traffic
Sarvotham, S., Riedi, R., and Baraniuk, R · 2001
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Optimization of collective communication operations in mpich
Thakur, R., Rabenseifner, R., and Gropp, W · 2005
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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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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 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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Staleness-aware async-sgd for distributed deep learning
Zhang, W., Gupta, S., Lian, X., and Liu, J · 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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Performance evaluation of deep learning tools in docker containers
Xu, P., Shi, S., and Chu, X · 2017
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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., Ramaswamy, S., Beaufays, F., Augenstein, S., Eichner, H., Kiddon, C., and Ramage, D · 2018
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A generic framework for privacy preserving deep learning
Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., and Passerat-Palmbach, J · 2018
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TensorFlow Federated , 2019
Ingerman, A. and Ostrowski, K · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Mg-wfbp: Efficient data communication for distributed synchronous sgd algorithms
Shi, S., Chu, X., and Li, B · 2019
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Flower: A friendly federated learning research framework
Beutel, D. J., Topal, T., Mathur, A., Qiu, X., Parcollet, T., and Lane, N. D · 2020
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Federated learning for predicting clinical outcomes in patients with covid-19
Dayan, I., Roth, H., and A. Zhong, e. a · 2020
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Fedml: A research library and benchmark for federated machine learning
He, C., Li, S., So, J., Zhang, M., Wang, H., Wang, X., Vepakomma, P., Singh, A., Qiu, H., Shen, L., Zhao, P., Kang, Y., Liu, Y., Raskar, R., Yang, Q., Annavaram, M., and Avestimehr, S · 2020
Tackling the objective inconsistency problem in heterogeneous federated optimization
Wang, J., Liu, Q., Liang, H., Joshi, G., and Poor, H. V · 2020
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Minibatch vs local sgd for heterogeneous distributed learning
Woodworth, B. E., Patel, K. K., and Srebro, N · 2020
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Federated learning based on dynamic regularization
Acar, D. A. E., Zhao, Y., Matas, R., Mattina, M., Whatmough, P., and Saligrama, V · 2021
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On bridging generic and personalized federated learning for image classification
Chen, H.-Y. and Chao, W.-L · 2021
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Exploiting shared representations for personalized federated learning
Collins, L., Hassani, H., Mokhtari, A., and Shakkottai, S · 2021
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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 · 2021
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Cited alongside, same era.
Federated visual classification with real-world data distribution
Hsu, T.-M. H., Qi, H., and Brown, M · 2020
Cited alongside, same era.
Albert: A lite bert for self-supervised learning of language representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R · 2020
Cited alongside, same era.
Federated optimization in heterogeneous networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V · 2020
Cited alongside, same era.
Think locally, act globally: Federated learning with local and global representations
Liang, P. P., Liu, T., Ziyin, L., Allen, N. B., Auerbach, R. P., Brent, D., Salakhutdinov, R., and Morency, L.-P · 2020
Cited alongside, same era.
Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
Cited alongside, same era.
Fedvision: An online visual object detection platform powered by federated learning
Liu, Y., Huang, A., Luo, Y., Huang, H., Liu, Y., Chen, Y., Feng, L., Chen, T., Yu, H., and Yang, Q · 2020
Cited alongside, same era.
The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H., Albarqouni, S., Bakas, S., Galtier, M., Landman, B., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R., Trask, A., Xu, D., Baust, M., and Cardoso, M · 2020
Cited alongside, same era.
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Fedscale: Benchmarking model and system performance of federated learning
Lai, F., Dai, Y., Zhu, X., Madhyastha, H. V., and Chowdhury, M · 2021
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No fear of heterogeneity: Classifier calibration for federated learning with non-IID data
Luo, M., Chen, F., Hu, D., Zhang, Y., Liang, J., and Feng, J · 2021
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Adaptive federated optimization
Reddi, S. J., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Konečný, J., Kumar, S., and McMahan, H. B · 2021
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Papaya: Practical, private, and scalable federated learning
Huba, D., Nguyen, J., Malik, K., Zhu, R., Rabbat, M., Yousefpour, A., Wu, C.-J., Zhan, H., Ustinov, P., Srinivas, H., Wang, K., Shoumikhin, A., Min, J., and Malek, M · 2022
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Virtual homogeneity learning: Defending against data heterogeneity in federated learning
Tang, Z., Zhang, Y., Shi, S., He, X., Han, B., and Chu, X · 2022
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Federatedscope: A flexible federated learning platform for heterogeneity
Xie, Y., Wang, Z., Chen, D., Gao, D., Yao, L., Kuang, W., Li, Y., Ding, B., and Zhou, J · 2022
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