Jakub Konečný, H. Brendan McMahan, Felix Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. Federated learning: strategies for improving communication efficiency. In NIPS Private Multi-Party Machine Learning Workshop, 2016.
Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, and Sebastian Stich. A linearly convergent algorithm for decentralized optimization: Sending less bits for free! In The 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021), 2021.
Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. Technical report, University of Toronto, Toronto, 2009.
Yann LeCun, Corinna Cortes, and CJ Burges. Mnist handwritten digit database. ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist, 2, 2010.
Xiaoyu Li and Francesco Orabona. A high probability analysis of adaptive SGD with momentum. arXiv preprint arXiv:2007.14294, 2020.
Xiaoyu Li, Mingrui Liu, and Francesco Orabona. On the last iterate convergence of momentum methods. In International Conference on Algorithmic Learning Theory, pages 699-717. PMLR, 2022a.
Xiaoyun Li, Belhal Karimi, and Ping Li. On distributed adaptive optimization with gradient compression. In International Conference on Learning Representations, 2022b.
Zhize Li, Dmitry Kovalev, Xun Qian, and Peter Richtárik. Acceleration for compressed gradient descent in distributed and federated optimization. In International Conference on Machine Learning, pages 5895-5904. PMLR, 2020.
Zhize Li, Hongyan Bao, Xiangliang Zhang, and Peter Richtárik. PAGE: A simple and optimal probabilistic gradient estimator for nonconvex optimization. In International Conference on Machine Learning, pages 6286-6295. PMLR, 2021.
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and Bill Dally. Deep gradient compression: Reducing the communication bandwidth for distributed training. In International Conference on Learning Representations, 2018.
Yanli Liu, Yuan Gao, and Wotao Yin. An improved analysis of stochastic gradient descent with momentum. In Advances in Neural Information Processing Systems, 2020.
Maksim Makarenko, Elnur Gasanov, Rustem Islamov, Abdurakhmon Sadiev, and Peter Richtarik. Adaptive compression for communication-efficient distributed training. arXiv preprint arXiv:2211.00188, Oct 2022.
Konstantin Mishchenko, Eduard Gorbunov, Martin Takáč, and Peter Richtárik. Distributed learning with compressed gradient differences. arXiv preprint arXiv:1901.09269, 2019.
Konstantin Mishchenko, Grigory Malinovsky, Sebastian Stich, and Peter Richtarik. ProxSkip: Yes! Local gradient steps provably lead to communication acceleration! Finally! In International Conference on Machine Learning, pages 15750-15769. PMLR, 2022.
Aritra Mitra, George J. Pappas, and Hamed Hassani. Temporal difference learning with compressed updates: Error-feedback meets reinforcement learning. arXiv preprint arXiv:2301.00944, 2023.
Constantin Philippenko and Aymeric Dieuleveut. Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees. arXiv preprint arXiv:2006.14591, 2020.
Maximilian Plattner. On SGD with momentum. Master's Thesis, 2022.
Boris T Polyak. Some methods of speeding up the convergence of iteration methods. Ussr computational mathematics and mathematical physics, 4(5):1-17, 1964.