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In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations.
Towards federated learning at scale: System design
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The renyi gaussian process: Towards improved generalization
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Federated learning with personalization layers
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Design and analysis of computer experiments
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Backpropagation convergence via deterministic nonmonotone perturbed minimization
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Predicting the output from a complex computer code when fast approximations are available
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A statistical method for tuning a computer code to a data base
Cox, D. D., Park, J.-S., and Singer, C. E. (2001) · 2001
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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) · 2001
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Fedsplit: An algorithmic framework for fast federated optimization
Pathak, R. and Wainwright, M. J. (2020) · 2005
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Fedpd: A federated learning framework with optimal rates and adaptivity to non-iid data
Zhang, X., Hong, M., Dhople, S., Yin, W., and Liu, Y. (2020) · 2005
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Accurate error bounds for the eigenvalues of the kernel matrix
Braun, M. L. (2006) · 2006
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Personalized federated learning with moreau envelopes
Dinh, C. T., Tran, N. H., and Nguyen, T. D. (2020) · 2006
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Gaussian processes for machine learning
Williams, C. K. and Rasmussen, C. E. (2006) · 2006
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Federated accelerated stochastic gradient descent
Yuan, H. and Ma, T. (2020) · 2006
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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. (2020a) · 2007
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Building efficient response surfaces of aerodynamic functions with kriging and cokriging
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Computed torque control with nonparametric regression models
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Model learning for robot control: a survey
Nguyen-Tuong, D. and Peters, J. (2011) · 2011
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Gaussian process single-index models as emulators for computer experiments
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Two-stage sensitivity-based group screening in computer experiments
Moon, H., Dean, A. M., and Santner, T. J. (2012) · 2012
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Deep gaussian processes
Damianou, A. and Lawrence, N. D. (2013) · 2013
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Gaussian processes for data-efficient learning in robotics and control
Deisenroth, M. P., Fox, D., and Rasmussen, C. E. (2013) · 2013
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A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis
Liu, K., Gebraeel, N. Z., and Shi, J. (2013) · 2013
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Parallelizing mcmc via weierstrass sampler
Wang, X. and Dunson, D. B. (2013) · 2013
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Sequential design and analysis of high-accuracy and low-accuracy computer codes
Xiong, S., Qian, P. Z., and Wu, C. J. (2013) · 2013
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Communication-efficient algorithms for statistical optimization
Zhang, Y., Duchi, J. C., and Wainwright, M. J. (2013) · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Building accurate emulators for stochastic simulations via quantile kriging
Plumlee, M. and Tuo, R. (2014) · 2014
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Cocoa: A general framework for communication-efficient distributed optimization
Smith, V., Forte, S., Chenxin, M., Takáč, M., Jordan, M. I., and Jaggi, M. (2018) · 2018
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Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach
Song, C. and Liu, K. (2018) · 2018
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How sgd selects the global minima in over-parameterized learning: A dynamical stability perspective
Wu, L., Ma, C., et al. (2018) · 2018
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Multifidelity aerodynamic optimization of a helicopter rotor blade
Bailly, J. and Bailly, D. (2019) · 2019
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Analyzing federated learning through an adversarial lens
Bhagoji, A. N., Chakraborty, S., Mittal, P., and Calo, S. (2019) · 2019
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Quantile regression under memory constraint
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Fernández-Godino, M. G., Park, C., Kim, N.-H., and Haftka, R. T. (2016) · 2016
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On large-batch training for deep learning: Generalization gap and sharp minima
Keskar, N. S., Mudigere, D., Nocedal, J., Smelyanskiy, M., and Tang, P. T. P. (2016) · 2016
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Prediction of porosity in metal-based additive manufacturing using spatial gaussian process models
Tapia, G., Elwany, A. H., and Sang, H. (2016) · 2016
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Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions
Yan, H., Liu, K., Zhang, X., and Shi, J. (2016) · 2016
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Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A. A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al. (2017) · 2017
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Communication-efficient sparse regression
Lee, J. D., Liu, Q., Sun, Y., and Taylor, J. E. (2017) · 2017
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Distributed learning with regularized least squares
Lin, S.-B., Guo, X., and Zhou, D.-X. (2017) · 2017
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Chen, X., Liu, W., and Zhang, Y. (2019) · 2019
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Blockchained on-device federated learning
Kim, H., Park, J., Bennis, M., and Kim, S.-L. (2019) · 2019
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Agnostic federated learning
Mohri, M., Sivek, G., and Suresh, A. T. (2019) · 2019
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Federated learning over wireless networks: Optimization model design and analysis
Tran, N. H., Bao, W., Zomaya, A., Nguyen, M. N., and Hong, C. S. (2019) · 2019
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Overview of gaussian process based multi-fidelity techniques with variable relationship between fidelities, application to aerospace systems
Brevault, L., Balesdent, M., and Hebbal, A. (2020) · 2020
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Stochastic gradient descent in correlated settings: A study on gaussian processes
Chen, H., Zheng, L., Al Kontar, R., and Raskutti, G. (2020) · 2020
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Heterogeneity-aware and communication-efficient distributed statistical inference
Duan, R., Ning, Y., and Chen, Y. (2020) · 2020
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Multi-robot active sensing and environmental model learning with distributed gaussian process
Jang, D., Yoo, J., Son, C. Y., Kim, D., and Kim, H. J. (2020) · 2020
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Binoculars for efficient, nonmyopic sequential experimental design
Jiang, S., Chai, H., Gonzalez, J., and Garnett, R. (2020) · 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) · 2020
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Federated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., and Smith, V. (2020) · 2020
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Why non-myopic bayesian optimization is promising and how far should we look-ahead? a study via rollout
Yue, X. and Kontar, R. A. (2020) · 2020
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Communication-efficient accurate statistical estimation
Fan, J., Guo, Y., and Wang, K. (2021) · 2021
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Enhanced gaussian process regression-based forecasting model for covid-19 outbreak and significance of iot for its detection
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Robust experimental designs for model calibration
Krishna, A., Joseph, V. R., Ba, S., Brenneman, W. A., and Myers, W. R. (2021) · 2021
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Ditto: Fair and robust federated learning through personalization
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Fed-ensemble: Improving generalization through model ensembling in federated learning
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Gifair-fl: An approach for group and individual fairness in federated learning
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