Decentralized Deep Learning with Arbitrary Communication Compression (1907.09356v3)

Abstract: Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches suffer from limited bandwidth of the network, we propose the use of communication compression in the decentralized training context. We show that Choco-SGD $-$ recently introduced and analyzed for strongly-convex objectives only $-$ converges under arbitrary high compression ratio on general non-convex functions at the rate $O\bigl(1/\sqrt{nT}\bigr)$ where $T$ denotes the number of iterations and $n$ the number of workers. The algorithm achieves linear speedup in the number of workers and supports higher compression than previous state-of-the art methods. We demonstrate the practical performance of the algorithm in two key scenarios: the training of deep learning models (i) over distributed user devices, connected by a social network and (ii) in a datacenter (outperforming all-reduce time-wise).

• The paper demonstrates that Choco-SGD effectively reduces communication overhead while achieving a linear speed-up in convergence relative to the number of workers.
• It provides rigorous theoretical analysis and empirical evaluations on benchmark datasets that confirm competitive test accuracy despite high compression ratios.
• The study highlights significant implications for privacy-preserving, on-device learning and scalable decentralized training in distributed environments.

Decentralized Deep Learning with Arbitrary Communication Compression

The paper "Decentralized Deep Learning with Arbitrary Communication Compression" addresses the challenge of reducing communication overhead in decentralized training of deep learning models. It introduces Choco-SGD, a decentralized optimization algorithm that leverages communication compression to facilitate efficient training on non-convex functions, even with non-IID data, while achieving linear speed-up in convergence relative to the number of workers. This is particularly beneficial when training over large and distributed compute networks or when ensuring data privacy by processing only local data, which is a cornerstone of decentralized machine learning frameworks.

Choco-SGD extends previous work on decentralized optimization by overcoming limitations in existing algorithms like DCD and ECD, which require unbiased compressors and are restricted to small compression ratios. The proposed algorithm supports arbitrary high compression ratios, thereby significantly reducing the data that needs to be exchanged between worker nodes during training.

Key Contributions and Experimental Setup

1. Theoretical Advances: The paper provides theoretical guarantees for Choco-SGD, demonstrating its convergence rate as $$\smash{\mathcal{O}\big(\frac{1}{\sqrt{nT}} + \frac{1}{(\rho^2 \delta T)^{2/3}} \big)}$$ where $n$ is the number of nodes, $\rho$ is the spectral gap of the mixing matrix, and $\delta$ denotes the compression ratio. The theoretical results show a linear speedup similar to centralized mini-batch SGD while maintaining robustness against the challenges posed by network topology and data compression.
2. Empirical Evaluation: The authors conduct experiments on benchmark datasets like CIFAR-10 and ImageNet, using models such as ResNet-20 and ResNet-50. They focus on realistic scenarios that simulate decentralized training over peer-to-peer networks and in datacenter environments. The performance is compared against baseline decentralized SGD without compression and centralized SGD with various compression schemes, including $\operatorname{sign}$ and $\operatorname{top}_a$ compression. Notably, Choco-SGD achieves competitive test accuracy with significantly reduced communication overhead.
3. Real-World Applications: The paper explores practical applications, including on-device learning over a peer-to-peer network topology and scaling to larger clusters in datacenter settings. These experiments highlight Choco-SGD's efficiency in scenarios where reducing communication costs is crucial, without significant loss in model accuracy.
4. Insights into Decentralized Training: The paper investigates the scaling of decentralized algorithms and pinpoints some shared deficiencies when scaling to a large number of nodes, noting the difficulty in matching the performance of centralized schemes at very large scales. While the results from decentralized setups indicate slower convergence or slightly reduced accuracy, they open avenues for future research to enhance the scalability and robustness of such frameworks.

Implications and Future Directions

The implications of this work are significant in contexts where data privacy is paramount, such as federated learning and on-device training. The ability to efficiently train models without centralized data aggregation helps in scenarios where data cannot be easily moved due to privacy concerns or bandwidth constraints. Moreover, the results suggest that decentralized learning can be made more viable in practice by judiciously employing communication compression techniques.

Future research could focus on overcoming the practical limitations encountered by existing decentralized schemes, particularly when scaling up the number of nodes or handling highly heterogeneous data distributions. Enhancements to Choco-SGD, such as adaptive compression strategies or integration with asynchronous methods, could further improve its suitability for large-scale training applications. Additionally, exploring new avenues for quantization and sparsification techniques aligned with specific model architectures and training regimes will be beneficial.

In conclusion, this paper lays foundational work for improving decentralized training processes via communication compression, making significant strides towards practical applications in privacy-preserving, distributed machine learning frameworks.