MARINA: Faster Non-Convex Distributed Learning with Compression

Eduard Gorbunov, Konstantin Burlachenko, Zhize Li, Peter Richtarik

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We develop and analyze MARINA: a new communication efficient method for non-convex distributed learning over heterogeneous datasets. MARINA employs a novel communication compression strategy based on the compression of gradient differences that is reminiscent of but different from the strategy employed in the DIANA method of Mishchenko et al. (2019). Unlike virtually all competing distributed first-order methods, including DIANA, ours is based on a carefully designed biased gradient estimator, which is the key to its superior theoretical and practical performance. The communication complexity bounds we prove for MARINA are evidently better than those of all previous first-order methods. Further, we develop and analyze two variants of MARINA: VR-MARINA and PP-MARINA. The first method is designed for the case when the local loss functions owned by clients are either of a finite sum or of an expectation form, and the second method allows for a partial participation of clients {–} a feature important in federated learning. All our methods are superior to previous state-of-the-art methods in terms of oracle/communication complexity. Finally, we provide a convergence analysis of all methods for problems satisfying the Polyak-{Ł}ojasiewicz condition.
Original languageEnglish (US)
Title of host publicationInternational Conference on Machine Learning
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-30
Acknowledgements: The work of Peter Richtarik, Eduard Gorbunov, Konstantin Burlachenko and Zhize Li was supported by KAUST Base-line Research Fund. The paper was written while E. Gorbunov was a research intern at KAUST. The work of E. Gorbunov in Sections 1, 2, and C was also partially supported by the Ministry of Science and Higher Education of the Russian Federation (Goszadaniye) 075-00337-20-03, projectNo. 0714-2020-0005, and in Sections 3, 4, D, E – byRFBR, project number 19-31-51001. We thank Konstantin Mishchenko (KAUST) for a suggestion related to the experiments, Elena Bazanova (MIPT) for the suggestions about improving the text, and Slavomır Hanzely (KAUST) and Egor Shulgin (KAUST) for spotting the typos.


Dive into the research topics of 'MARINA: Faster Non-Convex Distributed Learning with Compression'. Together they form a unique fingerprint.

Cite this