Langevin Monte Carlo via convex optimization. J. Mach. Learn. Res., 20:73–1, 2019. [EPK14] Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pages 1054–1067, 2014. [HKRC18] Ya-Ping Hsieh, Ali Kavis, Paul Rolland, and Volkan Cevher. Mirrored langevin dynamics. Advances In Neural Information Processing Systems 31 (Nips 2018), 31, 2018. [IVHW21] Pavel Izmailov, Sharad Vikram, Matthew D Hoffman, and Andrew Gordon Gordon Wilson. What are bayesian neural network posteriors really like? In International conference on machine learning, pages 4629–4640. PMLR, 2021. [KMA+21] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14(1–2):1–210, 2021. [KMY+16] Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016. [KR23] Avetik Karagulyan and Peter Richtárik. ELF: Federated Langevin algorithms with primal, dual and bidirectional compression. arXiv preprint arXiv:2303.04622, 2023. [Lam21] Andrew Lamperski. Projected stochastic gradient Langevin algorithms for constrained sampling and non-convex learning. In Conference on Learning Theory, pages 2891–2937. PMLR, 2021. [LZBG20] Fujun Luan, Shuang Zhao, Kavita Bala, and Ioannis Gkioulekas. Langevin monte carlo rendering with gradient-based adaptation. ACM Trans. Graph., 39(4):140, 2020. [Pic19] Sundar Pichai. Privacy should not be a luxury good. The New York Times, 8:25, 2019. [PMD23] Vincent Plassier, Eric Moulines, and Alain Durmus. Federated averaging langevin dynamics: Toward a unified theory and new algorithms. In International Conference on Artificial Intelligence and Statistics, pages 5299–5356. PMLR, 2023. [RC13] Christian Robert and George Casella. Monte Carlo statistical methods. Springer Science & Business Media, 2013. [Rob07] Christian Robert. The Bayesian choice: from decision-theoretic foundations to computational implementation. New York: Springer, 2007. [RRT17] Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky. Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis. In Satyen Kale and Ohad Shamir, editors, Proceedings of the 2017 Conference on Learning Theory, volume 65 of Proceedings of Machine Learning Research, pages 1674–1703, 07–10 Jul 2017. A. Karagulyan 29