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Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data.
Adaptive mixtures of local experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E · 1991
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Ensembles of biased classifiers
Khoussainov, R., Heß, A., and Kushmerick, N · 2005
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Core vector machines: Fast svm training on very large data sets
Tsang, I. W., Kwok, J. T., and Cheung, P.-M · 2005
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Model compression
Buciluǎ, C., Caruana, R., and Niculescu-Mizil, A · 2006
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Analysis of representations for domain adaptation
Ben-David, S., Blitzer, J., Crammer, K., and Pereira, F · 2007
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Attack of the tails: Yes, you really can backdoor federated learning
Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J.-y., Lee, K., and Papailiopoulos, D · 2007
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Learning bounds for domain adaptation
Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Wortman, J · 2008
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A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W · 2010
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Do deep nets really need to be deep?
Ba, J. and Caruana, R · 2014
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Privacy aware learning
Duchi, J. C., Jordan, M. I., and Wainwright, M. J · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 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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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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Emnist: Extending mnist to handwritten letters
Cohen, G., Afshar, S., Tapson, J., and Van Schaik, A · 2017
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Data-free knowledge distillation for deep neural networks
Lopes, R. G., Fenu, S., and Starner, T · 2017
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Cited alongside, same era.
cpsgd: Communication-efficient and differentially-private distributed sgd
Agarwal, N., Suresh, A. T., Yu, F., Kumar, S., and Mcmahan, H. B · 2018
Cited alongside, same era.
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
On convergence of distributed approximate newton methods: Globalization, sharper bounds and beyond
Yuan, X.-T. and Li, P · 2019
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Bayesian nonparametric federated learning of neural networks
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., and Khazaeni, Y · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Personalized federated learning with moreau envelopes
Dinh, C. T., Tran, N. H., and Nguyen, T. D · 2020
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Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
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Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., and Kim, S.-L · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S · 2018
Cited alongside, same era.
Wang, T., Zhu, J.-Y., Torralba, A., and Efros, A. A · 2018
Cited alongside, same era.
Federated collaborative filtering for privacy-preserving personalized recommendation system
Ammad-Ud-Din, M., Ivannikova, E., Khan, S. A., Oyomno, W., Fu, Q., Tan, K. E., and Flanagan, A · 2019
Cited alongside, same era.
Guha, N., Talwalkar, A., and Smith, V · 2019
Cited alongside, same era.
Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T.-M. H., Qi, H., and Brown, M · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Group knowledge transfer: Federated learning of large cnns at the edge
He, C., Annavaram, M., and Avestimehr, S · 2020
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Scaffold: Stochastic controlled averaging for federated learning
Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., and Suresh, A. T · 2020
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Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
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Three approaches for personalization with applications to federated learning
Mansour, Y., Mohri, M., Ro, J., and Suresh, A. T · 2020
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Federated learning’s blessing: Fedavg has linear speedup
Qu, Z., Lin, K., Kalagnanam, J., Li, Z., Zhou, J., and Zhou, Z · 2020
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Federated knowledge distillation
Seo, H., Park, J., Oh, S., Bennis, M., and Kim, S.-L · 2020
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Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., et al · 2020
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Federated model distillation with noise-free differential privacy
Sun, L. and Lyu, L · 2020
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Fedface: Collaborative learning of face recognition model
Aggarwal, D., Zhou, J., and Jain, A. K · 2021
Closest in time.
Fedbe: Making bayesian model ensemble applicable to federated learning
Chen, H.-Y. and Chao, W.-L · 2021
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Fedaux: Leveraging unlabeled auxiliary data in federated learning
Sattler, F. e. a · 2021
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Fedmix: Approximation of mixup under mean augmented federated learning
Yoon, T. e. a · 2021
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