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[1] Peter Richtárik,et al. Distributed Learning with Compressed Gradient Differences , 2019, ArXiv.
[2] Aurélien Lucchi,et al. Variance Reduced Stochastic Gradient Descent with Neighbors , 2015, NIPS.
[3] F. Bach,et al. Stochastic quasi-gradient methods: variance reduction via Jacobian sketching , 2018, Mathematical Programming.
[4] Eduard A. Gorbunov,et al. Linearly Converging Error Compensated SGD , 2020, NeurIPS.
[5] Mark W. Schmidt,et al. Variance-Reduced Methods for Machine Learning , 2020, Proceedings of the IEEE.
[6] Dan Alistarh,et al. QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks , 2016, 1610.02132.
[7] Aritra Dutta,et al. GRACE: A Compressed Communication Framework for Distributed Machine Learning , 2021, 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS).
[8] Sébastien Bubeck,et al. Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..
[9] Robert M. Gower,et al. Unified Analysis of Stochastic Gradient Methods for Composite Convex and Smooth Optimization , 2020, Journal of Optimization Theory and Applications.
[10] Ji Liu,et al. DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-Pass Error-Compensated Compression , 2019, ICML.
[11] Peter Richtárik,et al. A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent , 2019, AISTATS.
[12] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[13] Rong Jin,et al. Linear Convergence with Condition Number Independent Access of Full Gradients , 2013, NIPS.
[14] Xiaorui Liu,et al. A Double Residual Compression Algorithm for Efficient Distributed Learning , 2019, AISTATS.
[15] Anit Kumar Sahu,et al. Federated Learning: Challenges, Methods, and Future Directions , 2019, IEEE Signal Processing Magazine.
[16] Francis Bach,et al. SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives , 2014, NIPS.
[17] Laurent Condat,et al. Optimal Gradient Compression for Distributed and Federated Learning , 2020, ArXiv.
[18] Lin Xiao,et al. A Proximal Stochastic Gradient Method with Progressive Variance Reduction , 2014, SIAM J. Optim..
[19] Aymeric Dieuleveut,et al. Artemis: tight convergence guarantees for bidirectional compression in Federated Learning , 2020, ArXiv.
[20] Cong Xu,et al. TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning , 2017, NIPS.
[21] Peter Richtárik,et al. Parallel coordinate descent methods for big data optimization , 2012, Mathematical Programming.
[22] Ji Liu,et al. Gradient Sparsification for Communication-Efficient Distributed Optimization , 2017, NeurIPS.
[23] Suhas Diggavi,et al. Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations , 2019, IEEE Journal on Selected Areas in Information Theory.
[24] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[25] Peter Richtárik,et al. L-SVRG and L-Katyusha with Arbitrary Sampling , 2019, J. Mach. Learn. Res..
[26] Laurent Condat,et al. Proximal splitting algorithms: Relax them all! , 2019 .
[27] Sebastian U. Stich,et al. Stochastic Distributed Learning with Gradient Quantization and Variance Reduction , 2019, 1904.05115.
[28] Ahmed M. Abdelmoniem,et al. On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning , 2019, AAAI.
[29] Peter Richtárik,et al. Don't Jump Through Hoops and Remove Those Loops: SVRG and Katyusha are Better Without the Outer Loop , 2019, ALT.
[30] Richard Nock,et al. Advances and Open Problems in Federated Learning , 2021, Found. Trends Mach. Learn..
[31] Heinz H. Bauschke,et al. Convex Analysis and Monotone Operator Theory in Hilbert Spaces , 2011, CMS Books in Mathematics.
[32] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[33] Klaus-Robert Müller,et al. Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[34] Stephen P. Boyd,et al. Proximal Algorithms , 2013, Found. Trends Optim..