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

2025-05-14 · Alexander Tyurin, Danil Sivtsov

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.

📄 PDF Abstract BibTeX arXiv:2505.09218

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

Focus 설명 없음

SGD Stochastic Gradient Descent is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using…

BETULA: Numerically Stable CF-Trees for BIRCH Clustering

2020-06-23 · Andreas Lang, Erich Schubert

BIRCH clustering is a widely known approach for clustering, that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compres…

Clustering Data Compression

Estimating Individual Tree Height and Species from UAV Imagery

2026-03-24 · Jannik Endres, Etienne Laliberté, David Rolnick, Arthur Ouaknine arxiv

Accurate estimation of forest biomass, a major carbon sink, relies heavily on tree-level traits such as height and species. Unoccupied Aerial Vehicles (UAVs) capturing high-resolution imagery from a single RGB camera off…

Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset

2022-08-20 · Yuhua Yin, Julian Jang-Jaccard, Fariza Sabrina, Jin Kwak

Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP …

Anomaly Detection Clustering Intrusion Detection Pseudo Label

Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL

2018-11-30 · Bilal Kartal, Pablo Hernandez-Leal, Matthew E. taylor

Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power. However, there are still several challenges to be addressed such as convergence to l…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Applying BERT to Document Retrieval with Birch

2019-11-01 · IJCNLP 2019 11 · Zeynep Akkalyoncu Yilmaz, Shengjin Wang, Wei Yang, Haotian Zhang 외

We present Birch, a system that applies BERT to document retrieval via integration with the open-source Anserini information retrieval toolkit to demonstrate end-to-end search over large document collections. Birch imple…

Information Retrieval Retrieval