Fetching the paper…
Reading the bibliography…
Federated learning (FL) allows multiple clients to collaboratively learn a globally shared model through cycles of model aggregation and local model training, without the need to share data.
F. D. Foresee and M. T. Hagan, “Gauss-newton approximation to bayesian learning,” in Proceedings of International Conference on Neural Networks (ICNN’97) , vol. 3. IEEE, 1997, pp. 1930–1935
1935
Earlier work this paper cites.
Y. LeCun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Advances in neural information processing systems , 1990, pp. 598–605
1990
Earlier work this paper cites.
D. J. MacKay, “A practical bayesian framework for backpropagation networks,” Neural computation , vol. 4, no. 3, pp. 448–472, 1992
1992
Earlier work this paper cites.
S.-I. Amari, “Natural gradient works efficiently in learning,” Neural computation , vol. 10, no. 2, pp. 251–276, 1998
1998
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
H. Park, S.-I. Amari, and K. Fukumizu, “Adaptive natural gradient learning algorithms for various stochastic models,” Neural Networks , vol. 13, no. 7, pp. 755–764, 2000
2000
Earlier work this paper cites.
M. A. Carreira-Perpinan, “Mode-finding for mixtures of gaussian distributions,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 22, no. 11, pp. 1318–1323, 2000
2000
Earlier work this paper cites.
N. N. Schraudolph, “Fast curvature matrix-vector products for second-order gradient descent,” Neural computation , vol. 14, no. 7, pp. 1723–1738, 2002
2002
Earlier work this paper cites.
S. Ray, B. G. Lindsay et al. , “The topography of multivariate normal mixtures,” The Annals of Statistics , vol. 33, no. 5, pp. 2042–2065, 2005
2005
Earlier work this paper cites.
A. Krizhevsky, “Learning multiple layers of features from tiny images,” Master’s thesis, University of Tront , 2009
2009
Earlier work this paper cites.
J. Martens, “Deep learning via hessian-free optimization.” 2010
2010
Earlier work this paper cites.
S.-i. Amari, Differential-geometrical methods in statistics . Springer Science & Business Media, 2012, vol. 28
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
O. Shamir, N. Srebro, and T. Zhang, “Communication-efficient distributed optimization using an approximate newton-type method,” in International conference on machine learning , 2014, pp. 1000–1008
2014
Earlier work this paper cites.
S. Zhang, A. E. Choromanska, and Y. LeCun, “Deep learning with elastic averaging sgd,” in Advances in neural information processing systems , 2015, pp. 685–693
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
——, Second-order optimization for neural networks . University of Toronto (Canada), 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S.-W. Lee, J.-H. Kim, J. Jun, J.-W. Ha, and B.-T. Zhang, “Overcoming catastrophic forgetting by incremental moment matching,” in Advances in neural information processing systems , 2017, pp. 4652–4662
2017
Cited alongside, same era.
L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” in Advances in Neural Information Processing Systems , 2019, pp. 14 747–14 756
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
W. J. Maddox, P. Izmailov, T. Garipov, D. P. Vetrov, and A. G. Wilson, “A simple baseline for bayesian uncertainty in deep learning,” Advances in neural information processing systems , vol. 32, 2019
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Zenke, B. Poole, and S. Ganguli, “Continual learning through synaptic intelligence,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 3987–3995
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
H. Ritter, A. Botev, and D. Barber, “A scalable laplace approximation for neural networks,” in 6th International Conference on Learning Representations, ICLR 2018-Conference Track Proceedings , vol. 6. International Conference on Representation Learning, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2019, pp. 113–123
2019
Later among the works it cites.
H. Ritter, A. Botev, and D. Barber, “Online structured laplace approximations for overcoming catastrophic forgetting,” in Advances in Neural Information Processing Systems , 2018, pp. 3738–3748
2019
Later among the works it cites.
2020
Later among the works it cites.
J. Wang, Q. Liu, H. Liang, G. Joshi, and H. V. Poor, “Tackling the objective inconsistency problem in heterogeneous federated optimization,” Advances in neural information processing systems , vol. 33, pp. 7611–7623, 2020
2020
Later among the works it cites.
S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learning,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143
2020
Later among the works it cites.
M. Al-Shedivat, J. Gillenwater, E. Xing, and A. Rostamizadeh, “Federated learning via posterior averaging: A new perspective and practical algorithms,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
H.-Y. Chen and W.-L. Chao, “Fedbe: Making bayesian model ensemble applicable to federated learning,” in International Conference on Learning Representations , 2020
2020
Later among the works it cites.
L. Corinzia, A. Beuret, and J. M. Buhmann, “Variational federated multi-task learning,” 2021
2021
Closest in time.
2021
Closest in time.
Z. Huang, W. Shao, X. Wang, L. Lin, and P. Luo, “Rethinking the pruning criteria for convolutional neural network,” Advances in Neural Information Processing Systems , vol. 34, pp. 16 305–16 318, 2021
2021
Closest in time.
Q. Li, Y. Diao, Q. Chen, and B. He, “Federated learning on non-iid data silos: An experimental study,” in 2022 IEEE 38th International Conference on Data Engineering (ICDE) . IEEE, 2022, pp. 965–978
2022
Closest in time.