分布式机器学习研究将具有大规模数据量和计算量的任务分布式地部署到多台机器上,其核心思想在于“分而治之”,有效提高了大规模数据计算的速度并节省了开销。

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摘要: 编码计算将编码理论融于分布式计算中,利用灵活多样的编码方式降低数据洗牌造成的高通信负载,缓解掉队节点导致的计算延迟,有效提升分布式计算系统的整体性能,并通过纠错机制和数据掩藏等技术为分布式计算系统提供安全保障.鉴于其在通信、存储和计算复杂度等方面的优势,受到学术界的广泛关注,成为分布式计算领域的热门方向.对此,首先介绍编码计算的研究背景,明确编码计算的内涵与定义;随后对现有编码计算方案进行评述,从核心挑战入手,分别对面向通信瓶颈,计算延迟和安全隐私的编码计算方案展开介绍、总结和对比分析;最后指出未来可能的研究方向和技术挑战,为相关领域的研究提供有价值的参考.

https://crad.ict.ac.cn/CN/10.7544/issn1000-1239.2021.20210496

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Although the distributed machine learning methods can speed up the training of large deep neural networks, the communication cost has become the non-negligible bottleneck to constrain the performance. To address this challenge, the gradient compression based communication-efficient distributed learning methods were designed to reduce the communication cost, and more recently the local error feedback was incorporated to compensate for the corresponding performance loss. However, in this paper, we will show that a new "gradient mismatch" problem is raised by the local error feedback in centralized distributed training and can lead to degraded performance compared with full-precision training. To solve this critical problem, we propose two novel techniques, 1) step ahead and 2) error averaging, with rigorous theoretical analysis. Both our theoretical and empirical results show that our new methods can handle the "gradient mismatch" problem. The experimental results show that we can even train faster with common gradient compression schemes than both the full-precision training and local error feedback regarding the training epochs and without performance loss.

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