Communication Compression for Decentralized Training (1803.06443v5)

Abstract: Optimizing distributed learning systems is an art of balancing between computation and communication. There have been two lines of research that try to deal with slower networks: {\em communication compression} for low bandwidth networks, and {\em decentralization} for high latency networks. In this paper, We explore a natural question: {\em can the combination of both techniques lead to a system that is robust to both bandwidth and latency?} Although the system implication of such combination is trivial, the underlying theoretical principle and algorithm design is challenging: unlike centralized algorithms, simply compressing exchanged information, even in an unbiased stochastic way, within the decentralized network would accumulate the error and fail to converge. In this paper, we develop a framework of compressed, decentralized training and propose two different strategies, which we call {\em extrapolation compression} and {\em difference compression}. We analyze both algorithms and prove both converge at the rate of $O(1/\sqrt{nT})$ where $n$ is the number of workers and $T$ is the number of iterations, matching the convergence rate for full precision, centralized training. We validate our algorithms and find that our proposed algorithm outperforms the best of merely decentralized and merely quantized algorithm significantly for networks with {\em both} high latency and low bandwidth.

• The paper introduces ECD-PSGD and DCD-PSGD, hybrid algorithms that integrate communication compression with decentralization to achieve convergence comparable to centralized training.
• The paper develops a theoretical framework that ensures unbiased gradient compression and mitigates error accumulation with a convergence rate of O(1/√(nT)).
• Empirical results on CIFAR-10 using ResNet-20 validate that the proposed methods outperform existing decentralized models in high-latency, low-bandwidth network environments.

Communication Compression for Decentralized Training: An Analytical Overview

In the domain of distributed machine learning, optimizing the balance between computational workload and communication overhead is a pivotal challenge. The paper at hand, titled "Communication Compression for Decentralized Training," explores addressing this challenge by proposing a hybrid approach that combines two prominent strategies: communication compression and decentralization. While communication compression is particularly beneficial in low-bandwidth networks, decentralization helps mitigate high latency issues. This research posits the hypothesis that a combined framework inherits the strengths of both techniques to enhance robustness against latency and bandwidth limitations.

Theoretical Framework and Algorithmic Propositions

A principal contribution of the paper is the formulation of a theoretical framework that underpins the proposed algorithms, namely, Extrapolation Compression Decentralized Parallel Stochastic Gradient Descent (ECD-PSGD) and Difference Compression Decentralized Parallel Stochastic Gradient Descent (DCD-PSGD). The key theoretical insight is that a naive integration of compression within decentralized training can lead to error accumulation and non-convergence. To negate this, the authors introduce controlled mechanisms for integrating compression that maintain unbiasedness, despite the stochastic nature of compressed gradients.

Both ECD-PSGD and DCD-PSGD are rigorously analyzed, with convergence rates established at $O(1/\sqrt{nT})$, where $n$ is the number of workers and $T$ is the number of iterations. This convergence rate is on par with non-compressed centralized counterparts, signifying no inherent loss in performance from the compression techniques when appropriately applied. The distinction between the two algorithms lies in their approach: ECD-PSGD uses extrapolation of local models for compression while DCD-PSGD focuses on compressing model differences.

Empirical Validation and Results

The paper presents compelling empirical validation, demonstrating the superior performance of both algorithms in high-latency, low-bandwidth network conditions compared to existing decentralized and quantized models. The experimental setup involves CIFAR-10 dataset training on ResNet-20 across multiple network configurations. DCD-PSGD shows favorable convergence and performance comparable to centralized models until very aggressive compression levels are used, which cause divergence. Alternatively, ECD-PSGD maintains robustness even with extreme compression, albeit at a slight sacrifice in convergence rate compared to DCD-PSGD.

Implications and Future Directions

The implications of this research are multifold. Practically, the proposed algorithms pave the way for more efficient distributed training systems in environments characterized by severe communication constraints. Theoretically, this work extends the understanding of compression mechanisms in machine learning, offering insights into maintaining convergence in altered network circumstances.

Future research could build on these findings by exploring the scalability of these algorithms in diverse architectures beyond ring topologies, such as mesh or arbitrary graphs. Additionally, extending the algorithmic framework to support unbiased compression techniques like sparsification could offer further reductions in communication overhead.

In conclusion, the paper makes a significant contribution to the field of distributed machine learning by intricately balancing communication and computation through sophisticated algorithmic strategies. As distributed architectures continue to proliferate, the integration of such frameworks will likely be indispensable for efficient large-scale machine learning model training.