Faster Rates for Compressed Federated Learning with Client-Variance Reduction

Haoyu Zhao, Konstantin Burlachenko, Zhize Li, Peter Richtárik

Type: Article

Publication Date: 2024-03-11

Citations: 0

DOI: https://doi.org/10.1137/23m1553820

View Publication

Locations

SIAM Journal on Mathematics of Data Science - View
arXiv (Cornell University) - View - PDF

Similar Works

Action	Title	Year	Authors
+	Faster Rates for Compressed Federated Learning with Client-Variance Reduction	2021	Haoyu Zhao Konstantin Burlachenko Zhize Li Peter Richtárik
+	Artemis: tight convergence guarantees for bidirectional compression in Federated Learning.	2020	Constantin Philippenko Aymeric Dieuleveut
+	MARINA: Faster Non-Convex Distributed Learning with Compression	2021	Eduard Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik
+	MARINA: Faster Non-Convex Distributed Learning with Compression	2021	Eduard Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik
+	MARINA: Faster Non-Convex Distributed Learning with Compression	2021	Eduard Gorbunov Konstantin Burlachenko Zhize Li Peter Richtárik
+	Federated Learning with Compression: Unified Analysis and Sharp Guarantees	2020	Farzin Haddadpour Mohammad Mahdi Kamani Aryan Mokhtari Mehrdad Mahdavi
+	CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning	2021	Haibo Yang Jia Liu Elizabeth Serena Bentley
+ PDF Chat	CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning	2021	Haibo Yang Jia Liu Elizabeth Serena Bentley
+	Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees	2020	Constantin Philippenko Aymeric Dieuleveut
+	Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression	2022	Xiaoyun Li Ping Li
+	Federated Learning with Compression: Unified Analysis and Sharp Guarantees	2020	Farzin Haddadpour Mohammad Mahdi Kamani Aryan Mokhtari Mehrdad Mahdavi
+	TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation	2023	Laurent Condat Grigory Malinovsky Peter Richtárik
+	Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression	2022	Laurent Condat Ivan Agarský Peter Richtárik
+	Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees	2020	Constantin Philippenko Aymeric Dieuleveut
+	Communication-Efficient Adaptive Federated Learning	2022	Yujia Wang Lu Lin Jinghui Chen
+	Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization	2020	Zhize Li Dmitry Kovalev Xun Qian Peter Richtárik
+	Better Methods and Theory for Federated Learning: Compression, Client Selection and Heterogeneity	2022	Samuel Horváth
+ PDF Chat	Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes	2024	Xiaoxin Su Yipeng Zhou Laizhong Cui John C. S. Lui Jiangchuan Liu
+ PDF Chat	Communication-efficient Vertical Federated Learning via Compressed Error Feedback	2024	Pedro Valdeira João Xavier Cláudia Soares Yuejie Chi
+	Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates	2023	Ahmad Rammal Kaja Gruntkowska Nikita Fedin Eduard Gorbunov Peter Richtárik

Works That Cite This (0)

Action	Title	Year	Authors

Works Cited by This (1)

Action	Title	Year	Authors
+ PDF Chat	Artificial intelligence and statistics	2018	Bin Yu Karl Kumbier