Fetching the paper…
Reading the bibliography…
In federated learning, clients share a global model that has been trained on decentralized local client data.
Adaptive mixtures of local experts
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E · 1991
Earlier work this paper cites.
Ag’s corpus of news articles
Gulli, A · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Domain adaptation: Learning bounds and algorithms
Mansour, Y., Mohri, M., and Rostamizadeh, A · 2009
Earlier work this paper cites.
Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Learning and transferring mid-level image representations using convolutional neural networks
Oquab, M., Bottou, L., Laptev, I., and Sivic, J · 2014
Earlier work this paper cites.
Privacy-preserving deep learning
Shokri, R. and Shmatikov, V · 2015
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Konečnỳ, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
Earlier work this paper cites.
Decentralized collaborative learning of personalized models over networks
Vanhaesebrouck, P., Bellet, A., and Tommasi, M · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
Cited alongside, same era.
Personalized and private peer-to-peer machine learning
Bellet, A., Guerraoui, R., Taziki, M., and Tommasi, M · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction, 2018
Hard, A., Kiddon, C. M., Ramage, D., Beaufays, F., Eichner, H., Rao, K., Mathews, R., and Augenstein, S · 2018
Cited alongside, same era.
Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., and Kim, S.-L · 2018
Cited alongside, same era.
Private federated learning with domain adaptation
Peterson, D., Kanani, P., and Marathe, V. J · 2019
Later among the works it cites.
Federated evaluation of on-device personalization
Wang, K., Mathews, R., Kiddon, C., Eichner, H., Beaufays, F., and Ramage, D · 2019
Later among the works it cites.
Beyond inferring class representatives: User-level privacy leakage from federated learning
Wang, Z., Song, M., Zhang, Z., Song, Y., Wang, Q., and Qi, H · 2019
Later among the works it cites.
Bayesian nonparametric federated learning of neural networks
Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., and Khazaeni, Y · 2019
Later among the works it cites.
Adaptive personalized federated learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Federated learning with personalization layers
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., and Choudhary, S · 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.
Improving federated learning personalization via model agnostic meta learning
Jiang, Y., Konečnỳ, J., Rush, K., and Kannan, S · 2019
Cited alongside, same era.
Survey on deep learning with class imbalance
Johnson, J. M. and Khoshgoftaar, T. 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.
Closest in time.
Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2020
Closest in time.
Federated learning of a mixture of global and local models
Hanzely, F. and Richtárik, P · 2020
Closest in time.
Group knowledge transfer: Collaborative training of large cnns on the edge
He, C., Avestimehr, S., and Annavaram, M · 2020
Closest in time.
The non-iid data quagmire of decentralized machine learning
Hsieh, K., Phanishayee, A., Mutlu, O., and Gibbons, P · 2020
Closest in time.
Ensemble distillation for robust model fusion in federated learning
Lin, T., Kong, L., Stich, S. U., and Jaggi, M · 2020
Closest in time.