Eduard Gorbunov - “MARINA: Faster Non-Convex Distributed Learning with Compression“ | MoCCA’20

The talk “MARINA: Faster Non-Convex Distributed Learning with Compression“ by Eduard Gorbunov on the Moscow Conference on Combinatorics and Applications at MIPT. Annotation: 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 which 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. To the best of our knowledge, the communication complexity bounds we prove for MARINA are strictly superior to 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 desi