[31] Ankit Garg, Tengyu Ma, and Huy Nguyen. On communication cost of distributed statistical estimation and dimensionality. Advances in Neural Information Processing Systems, 27, 2014.
[32] Wei-Ning Chen, Peter Kairouz, and Ayfer Ozgur. Breaking the communication-privacy-accuracy trilemma. Advances in Neural Information Processing Systems, 33:3312–3324, 2020.
[33] Prathamesh Mayekar, Ananda Theertha Suresh, and Himanshu Tyagi. Wyner-ziv estimators: Efficient distributed mean estimation with side-information. In International Conference on Artificial Intelligence and Statistics, pages 3502-3510. PMLR, 2021.
[34] Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, and Michael Mitzenmacher. Drive: One-bit distributed mean estimation. Advances in Neural Information Processing Systems, 34:362–377, 2021.
[35] Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben Itzhak, and Michael Mitenmacher. Eden: Communication-efficient and robust distributed mean estimation for federated learning. In International Conference on Machine Learning, pages 21984-22014. PMLR, 2022.
[36] Kai Liang and Youlong Wu. Improved communication efficiency for distributed mean estimation with side information. In 2021 IEEE International Symposium on Information Theory (ISIT), pages 3185-3190. IEEE, 2021.
[37] Nir Ailon and Bernard Chazelle. Approximate nearest neighbors and the fast johnson-lindenstrauss transform. In Proceedings of the Thirty-Eighth Annual ACM Symposium on Theory of Computing, STOC '06, page 557-563, New York, NY, USA, 2006. Association for Computing Machinery.
[38] Joel A. Tropp. Improved analysis of the subsampled randomized hadamard transform, 2011.
[39] Jonathan Lacotte, Sifan Liu, Edgar Dobriban, and Mert Pilanci. Optimal iterative sketching with the subsampled randomized hadamard transform, 2020.
[40] Oleg Balabanov, Matthias Beaupère, Laura Grigori, and Victor Lederer. Block subsampled randomized Hadamard transform for low-rank approximation on distributed architectures. working paper or preprint, October 2022.
[41] Christos Boutsidis and Alex Gittens. Improved matrix algorithms via the subsampled randomized hadamard transform, 2013.
[42] Yichao Lu, Paramveer Dhillon, Dean P Foster, and Lyle Ungar. Faster ridge regression via the subsampled randomized hadamard transform. In C.J. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013.
[43] Mostafa Haghir Chehreghani. Subsampled randomized hadamard transform for regression of dynamic graphs. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM '20, page 2045-2048, New York, NY, USA, 2020. Association for Computing Machinery.
[44] Dan Teng, Xiaowei Zhang, Li Cheng, and Delin Chu. Least squares approximation via sparse subsampled randomized hadamard transform. IEEE Transactions on Big Data, 8(2):446-457, 2022.
[45] Jonathan Lacotte and Mert Pilanci. Optimal randomized first-order methods for least-squares problems, 2020.
[46] Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Ion Stoica, Raman Arora, et al. Communication-efficient distributed sgd with sketching. Advances in Neural Information Processing Systems, 32, 2019.
[47] Farzin Haddadpour, Belhal Karimi, Ping Li, and Xiaoyun Li. Fedsketch: Communication-efficient and private federated learning via sketching. arXiv preprint arXiv:2008.04975, 2020.