Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory-Reference-Cited by-同舟云学术

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory

Published:2024-07-15 Issue: Volume: Page:1-19
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Liang Jiajun¹,Zhang Qian²,Deng Wei³,Song Qifan²,Lin Guang⁴

Affiliation:

1. ByteDance Inc, China

2. Department of Statistics, Purdue University, West Lafayette, IN

3. Machine Learning Research, Morgan Stanley, New York, NY

4. Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, IN

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2024.2380051

Reference43 articles.

1. Akyildiz Ö. D. and Sabanis S. (2020) “Nonasymptotic Analysis of Stochastic Gradient Hamiltonian Monte Carlo under Local Conditions for Nonconvex Optimization ” arXiv preprint arXiv:2002.05465 .

2. Bonawitz K. Eichner H. Grieskamp W. Huba D. Ingerman A. Ivanov V. Kiddon C. Konečnỳ J. Mazzocchi S. and McMahan B. (2019) “Towards Federated Learning at Scale: System design ” Proceedings of Machine Learning and Systems 1 374–388.

3. Brier G. W. et al. (1950) “Verification of Forecasts Expressed in Terms of Probability ” Monthly weather review 78 1–3.

4. Chau H. N. and Rasonyi M. (2022) “Stochastic gradient Hamiltonian Monte Carlo for non-convex learning ” Stochastic Processes and their Applications 149 341–368.

5. Chen M. Yang Z. Saad W. Yin C. Poor H. V. and Cui S. (2020a) “A Joint Learning and Communications Framework for Federated Learning over Wireless Networks ” IEEE Transactions on Wireless Communications 20 269–283.