Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods

• URL: http://arxiv.org/abs/2505.09218v2
• Date: Sun, 25 May 2025 12:36:59 GMT
• Title: Birch SGD: A Tree Graph Framework for Local and Asynchronous SGD Methods
• Authors: Alexander Tyurin, Danil Sivtsov,
• Abstract summary: We propose a new unifying framework, Birch SGD, for analyzing and designing distributed SGD methods.<n>Using Birch SGD, we design eight new methods and analyze them alongside previously known ones, with at least six of the new methods shown to have optimal computational time complexity.<n>Our research leads to two key insights: (i) all methods share the same "iteration rate" of $Oleft(frac(R + 1) L Deltavarepsilon + fracsigma2 L Deltavarepsilon2right)$, where $R$
• Score: 51.54704494242525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new unifying framework, Birch SGD, for analyzing and designing distributed SGD methods. The central idea is to represent each method as a weighted directed tree, referred to as a computation tree. Leveraging this representation, we introduce a general theoretical result that reduces convergence analysis to studying the geometry of these trees. This perspective yields a purely graph-based interpretation of optimization dynamics, offering a new and intuitive foundation for method development. Using Birch SGD, we design eight new methods and analyze them alongside previously known ones, with at least six of the new methods shown to have optimal computational time complexity. Our research leads to two key insights: (i) all methods share the same "iteration rate" of $O\left(\frac{(R + 1) L \Delta}{\varepsilon} + \frac{\sigma^2 L \Delta}{\varepsilon^2}\right)$, where $R$ the maximum "tree distance" along the main branch of a tree; and (ii) different methods exhibit different trade-offs-for example, some update iterates more frequently, improving practical performance, while others are more communication-efficient or focus on other aspects. Birch SGD serves as a unifying framework for navigating these trade-offs. We believe these results provide a unified foundation for understanding, analyzing, and designing efficient asynchronous and parallel optimization methods.

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