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Journal Articles
- 2024
[j3]Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik
:
Faster Rates for Compressed Federated Learning with Client-Variance Reduction. SIAM J. Math. Data Sci. 6(1): 154-175 (2024)- 2023
- [j2]El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik:
Personalized Federated Learning with Communication Compression. Trans. Mach. Learn. Res. 2023 (2023) - [j1]Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik:
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. Trans. Mach. Learn. Res. 2023 (2023)
Conference and Workshop Papers
- 2024
- [c4]Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko:
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants. ICLR 2024 - 2023
- [c3]Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik:
Federated Learning is Better with Non-Homomorphic Encryption. DistributedML@CoNEXT 2023: 49-84 - 2021
- [c2]Konstantin Burlachenko, Samuel Horváth, Peter Richtárik:
FL_PyTorch: optimization research simulator for federated learning. DistributedML@CoNEXT 2021: 1-7 - [c1]Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
MARINA: Faster Non-Convex Distributed Learning with Compression. ICML 2021: 3788-3798
Informal and Other Publications
- 2024
- [i11]Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko:
Error Feedback Reloaded: From Quadratic to Arithmetic Mean of Smoothness Constants. CoRR abs/2402.10774 (2024) - [i10]Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, Peter Richtárik:
PV-Tuning: Beyond Straight-Through Estimation for Extreme LLM Compression. CoRR abs/2405.14852 (2024) - 2023
- [i9]Grigory Malinovsky, Samuel Horváth, Konstantin Burlachenko, Peter Richtárik:
Federated Learning with Regularized Client Participation. CoRR abs/2302.03662 (2023) - [i8]Peter Richtárik, Elnur Gasanov, Konstantin Burlachenko:
Error Feedback Shines when Features are Rare. CoRR abs/2305.15264 (2023) - [i7]Konstantin Burlachenko, Abdulmajeed Alrowithi, Fahad Ali Albalawi, Peter Richtárik:
Federated Learning is Better with Non-Homomorphic Encryption. CoRR abs/2312.02074 (2023) - 2022
- [i6]Konstantin Burlachenko, Samuel Horváth, Peter Richtárik:
FL_PyTorch: optimization research simulator for federated learning. CoRR abs/2202.03099 (2022) - [i5]Alexander Tyurin, Lukang Sun, Konstantin Burlachenko, Peter Richtárik:
Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling. CoRR abs/2206.02275 (2022) - [i4]Abdurakhmon Sadiev, Grigory Malinovsky, Eduard Gorbunov, Igor Sokolov, Ahmed Khaled, Konstantin Burlachenko, Peter Richtárik:
Federated Optimization Algorithms with Random Reshuffling and Gradient Compression. CoRR abs/2206.07021 (2022) - [i3]El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik:
Personalized Federated Learning with Communication Compression. CoRR abs/2209.05148 (2022) - 2021
- [i2]Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
MARINA: Faster Non-Convex Distributed Learning with Compression. CoRR abs/2102.07845 (2021) - [i1]Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik:
Faster Rates for Compressed Federated Learning with Client-Variance Reduction. CoRR abs/2112.13097 (2021)
Coauthor Index
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