[3] L. P. Barnes, H. A. Inan, B. Isik, and A. Özgür. rtop-k: A statistical estimation approach to distributed sgd. IEEE Journal on Selected Areas in Information Theory, 1(3):897-907, 2020.
[4] A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers. Protection against reconstruction and its applications in private federated learning. arXiv preprint arXiv:1812.00984, 2018.
[5] S. Chatterjee and P. Diaconis. The sample size required in importance sampling. The Annals of Applied Probability, 28(2):1099-1135, 2018.
[6] W.-N. Chen, P. Kairouz, and A. Ozgur. Breaking the communication-privacy-accuracy trilemma. Advances in Neural Information Processing Systems, 33:3312–3324, 2020.
[7] J. Duchi and R. Rogers. Lower bounds for locally private estimation via communication complexity. In Conference on Learning Theory, pages 1161-1191. PMLR, 2019.
[8] J. C. Duchi, M. I. Jordan, and M. J. Wainwright. Local privacy and statistical minimax rates. In 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pages 429-438. IEEE, 2013.
[9] J. C. Duchi, M. I. Jordan, and M. J. Wainwright. Minimax optimal procedures for locally private estimation. Journal of the American Statistical Association, 113(521):182-201, 2018.
[10] C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography: Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006. Proceedings 3, pages 265-284. Springer, 2006.
[11] V. Feldman, J. Nelson, H. Nguyen, and K. Talwar. Private frequency estimation via projective geometry. In International Conference on Machine Learning, pages 6418-6433. PMLR, 2022.
[12] V. Feldman and K. Talwar. Lossless compression of efficient private local randomizers. In International Conference on Machine Learning, pages 3208-3219. PMLR, 2021.
[13] V. Gandikota, D. Kane, R. K. Maity, and A. Mazumdar. vqsgd: Vector quantized stochastic gradient descent. In International Conference on Artificial Intelligence and Statistics, pages 2197-2205. PMLR, 2021.
[14] S. Ghadimi and G. Lan. Stochastic first-and zeroth-order methods for nonconvex stochastic programming. SIAM Journal on Optimization, 23(4):2341-2368, 2013.
[15] M. Havasi, R. Peharz, and J. M. Hernández-Lobato. Minimal random code learning: Getting bits back from compressed model parameters. In International Conference on Learning Representations, 2019.
[16] Z. Huang, Y. Liang, and K. Yi. Instance-optimal mean estimation under differential privacy. Advances in Neural Information Processing Systems, 34:25993-26004, 2021.
[17] B. Isik, F. Pase, D. Gunduz, S. Koyejo, T. Weissman, and M. Zorzi. Communication-efficient federated learning through importance sampling. arXiv preprint arXiv:2306.12625, 2023.
[18] B. Isik, F. Pase, D. Gunduz, T. Weissman, and Z. Michele. Sparse random networks for communication-efficient federated learning. In The Eleventh International Conference on Learning Representations, 2023.
[19] B. Isik and T. Weissman. Learning under storage and privacy constraints. In 2022 IEEE International Symposium on Information Theory (ISIT), pages 1844-1849. IEEE, 2022.
[20] B. Isik and T. Weissman. Lossy compression of noisy data for private and data-efficient learning. IEEE Journal on Selected Areas in Information Theory, 2023.
[21] B. Isik, T. Weissman, and A. No. An information-theoretic justification for model pruning. In International Conference on Artificial Intelligence and Statistics, pages 3821-3846. PMLR, 2022.