MARINA: Faster Non-Convex Distributed Learning with Compression

by   Eduard Gorbunov, et al.

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 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 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 the oracle/communication complexity. Finally, we provide convergence analysis of all methods for problems satisfying the Polyak-Lojasiewicz condition.


page 1

page 2

page 3

page 4


DASHA: Distributed Nonconvex Optimization with Communication Compression, Optimal Oracle Complexity, and No Client Synchronization

We develop and analyze DASHA: a new family of methods for nonconvex dist...

Improving Accelerated Federated Learning with Compression and Importance Sampling

Federated Learning is a collaborative training framework that leverages ...

Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation

Despite their high computation and communication costs, Newton-type meth...

CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression

Due to the high communication cost in distributed and federated learning...

Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top

Byzantine-robustness has been gaining a lot of attention due to the grow...

Permutation Compressors for Provably Faster Distributed Nonconvex Optimization

We study the MARINA method of Gorbunov et al (2021) – the current state-...

GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity

In this work, we study distributed optimization algorithms that reduce t...

Please sign up or login with your details

Forgot password? Click here to reset