MARINA: Faster Non-Convex Distributed Learning with Compression

Abstract

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-Lojasiewicz condition.

ICML 2021; v2 contains additional experiments; v3 contains small fixes of the typos in the complexity results for online case for VR-MARINA; 42 pages, 6 figures, 3 algorithms

References in corpus (9)

• Error Feedback Fixes SignSGD and other Gradient Compression Schemes
• Optimization for deep learning: theory and algorithms
• Stochastic Distributed Learning with Gradient Quantization and Variance Reduction
• Linearly Converging Error Compensated SGD
• Federated Learning with Compression: Unified Analysis and Sharp Guarantees
• A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimization
• PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
• Faster Non-Convex Federated Learning via Global and Local Momentum
• Distributed Stochastic Non-Convex Optimization: Momentum-Based Variance Reduction

Cited by in corpus (8)

• EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback
• PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
• FedNL: Making Newton-Type Methods Applicable to Federated Learning
• FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learning
• Activations and Gradients Compression for Model-Parallel Training
• Optimal Data Splitting in Distributed Optimization for Machine Learning
• Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices
• Secure Distributed Training at Scale