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Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution.
Maximum likelihood estimation of misspecified models
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Gradient-based learning applied to document recognition
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A model of inductive bias learning
Baxter, J · 2000
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Personalized federated learning: A meta-learning approach
Fallah, A., Mokhtari, A., and Ozdaglar, A · 2002
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Acceleration for compressed gradient descent in distributed and federated optimization
Li, Z., Kovalev, D., Qian, X., and Richtárik, P · 2002
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Adaptive principal components and image denoising
Muresan, D. D. and Parks, T. W · 2003
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Visualizing data using t-sne
Maaten, L. V. D. and Hinton, G. E · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Privacy aware learning
Duchi, J. C., Jordan, M. I., and Wainwright, M. J · 2014
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A dynamic convolutional layer for short rangeweather prediction
Klein, B., Wolf, L., and Afek, Y · 2015
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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Conditioned regression models for non-blind single image super-resolution
Riegler, G., Schulter, S., Rüther, M., and Bischof, H · 2015
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
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Ha, D., Dai, A. M., and Le, Q. V · 2017
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Meta-sgd: Learning to learn quickly for few-shot learning
Li, Z., Zhou, F., Chen, F., and Li, H · 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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Character-level language modeling with recurrent highway hypernetworks
Suarez, J · 2017
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Zhou, F. and Cong, G · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
Agarwal, N., Suresh, A. T., Yu, F. X. X., Kumar, S., and McMahan, B · 2018
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Don’t use large mini-batches, use local sgd
Lin, T., Stich, S. U., Patel, K. K., and Jaggi, M · 2018
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Stochastic hyperparameter optimization through hypernetworks
Lorraine, J. and Duvenaud, D · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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On first-order meta-learning algorithms
Nichol, A., Achiam, J., and Schulman, J · 2018
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On the convergence of federated optimization in heterogeneous networks
Sahu, A. K., Li, T., Sanjabi, M., Zaheer, M., Talwalkar, A., and Smith, V · 2018
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Local sgd converges fast and communicates little
Stich, S. U · 2018
Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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Lookahead optimizer: k steps forward, 1 step back
Zhang, M., Lucas, J., Ba, J., and Hinton, G. E · 2019
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Efficient meta learning via minibatch proximal update
Zhou, P., Yuan, X., Xu, H., Yan, S., and Feng, J · 2019
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Federated heavy hitters discovery with differential privacy
Zhu, W., Kairouz, P., Sun, H., McMahan, B., and Li, W · 2019
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Delta-stn: Efficient bilevel optimization for neural networks using structured response jacobians
Bae, J. and Grosse, R. B · 2020
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Cited alongside, same era.
Wang, J. and Joshi, G · 2018
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Federated learning with non-iid data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V · 2018
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Federated learning with personalization layers
Arivazhagan, M. G., Aggarwal, V., Singh, A. K., and Choudhary, S · 2019
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Alpha maml: Adaptive model-agnostic meta-learning
Behl, H. S., Baydin, A. G., and Torr, P. H · 2019
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Hyper-sphere quantization: Communication-efficient sgd for federated learning
Dai, X., Yan, X., Zhou, K., Yang, H., Ng, K. K., Cheng, J., and Fan, Y · 2019
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On the convergence of local descent methods in federated learning
Haddadpour, F. and Mahdavi, M · 2019
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Measuring the effects of non-identical data distribution for federated visual classification
Hsu, T. H., Qi, H., and Brown, M · 2019
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Qsparse-local-sgd: Distributed sgd with quantization, sparsification, and local computations
Basu, D., Data, D., Karakus, C., and Diggavi, S. N · 2020
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Adaptive personalized federated learning
Deng, Y., Kamani, M. M., and Mahdavi, M · 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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Federated learning of a mixture of global and local models
Hanzely, F. and Richtárik, P · 2020
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Personalized cross-silo federated learning on non-iid data
Huang, Y., Chu, L., Zhou, Z., Wang, L., Liu, J., Pei, J., and Zhang, Y · 2020
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Faster on-device training using new federated momentum algorithm
Huo, Z., Yang, Q., Gu, B., Huang, L. C., et al · 2020
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Survey of personalization techniques for federated learning
Kulkarni, V., Kulkarni, M., and Pant, A · 2020
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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
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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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Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Reisizadeh, A., Mokhtari, A., Hassani, H., Jadbabaie, A., and Pedarsani, R · 2020
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Personalized federated learning for intelligent iot applications: A cloud-edge based framework
Wu, Q., He, K., and Chen, X · 2020
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Personalized federated learning with first order model optimization
Zhang, M., Sapra, K., Fidler, S., Yeung, S., and Alvarez, J. M · 2020
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A survey on security and privacy of federated learning
Mothukuri, V., Parizi, R. M., Pouriyeh, S., Huang, Y., Dehghantanha, A., and Srivastava, G · 2021
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Learning the pareto front with hypernetworks
Navon, A., Shamsian, A., Chechik, G., and Fetaya, E · 2021
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