The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication

• URL: http://arxiv.org/abs/2405.11667v1
• Date: Sun, 19 May 2024 20:20:03 GMT
• Title: The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
• Authors: Kumar Kshitij Patel, Margalit Glasgow, Ali Zindari, Lingxiao Wang, Sebastian U. Stich, Ziheng Cheng, Nirmit Joshi, Nathan Srebro,
• Abstract summary: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice. We provide new lower bounds for local SGD under existing first-order data heterogeneity assumptions. We also show the min-max optimality of accelerated mini-batch SGD for several problem classes.
• Score: 37.210933391984014
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD. Despite this success, theoretically proving the dominance of local SGD in settings with reasonable data heterogeneity has been difficult, creating a significant gap between theory and practice. In this paper, we provide new lower bounds for local SGD under existing first-order data heterogeneity assumptions, showing that these assumptions are insufficient to prove the effectiveness of local update steps. Furthermore, under these same assumptions, we demonstrate the min-max optimality of accelerated mini-batch SGD, which fully resolves our understanding of distributed optimization for several problem classes. Our results emphasize the need for better models of data heterogeneity to understand the effectiveness of local SGD in practice. Towards this end, we consider higher-order smoothness and heterogeneity assumptions, providing new upper bounds that imply the dominance of local SGD over mini-batch SGD when data heterogeneity is low.

Related papers

• Stability and Generalization for Distributed SGDA [70.97400503482353]
  We propose the stability-based generalization analytical framework for Distributed-SGDA. We conduct a comprehensive analysis of stability error, generalization gap, and population risk across different metrics. Our theoretical results reveal the trade-off between the generalization gap and optimization error.
  arXiv  Detail & Related papers  (2024-11-14T11:16:32Z)
• Why (and When) does Local SGD Generalize Better than SGD? [46.993699881100454]
  Local SGD is a communication-efficient variant of SGD for large-scale training. This paper aims to understand why (and when) Local SGD generalizes better based on Differential Equation (SDE) approximation.
  arXiv  Detail & Related papers  (2023-03-02T12:56:52Z)
• Federated Minimax Optimization: Improved Convergence Analyses and Algorithms [32.062312674333775]
  We consider non minimax optimization, is gaining prominence many modern machine learning applications such as GANs. We provide a novel and tighter analysis algorithm, improves convergence communication guarantees in the existing literature.
  arXiv  Detail & Related papers  (2022-03-09T16:21:31Z)
• Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning [58.79085525115987]
  Local methods are one of the promising approaches to reduce communication time. We show that the communication complexity is better than non-local methods when the local datasets is smaller than the smoothness local loss.
  arXiv  Detail & Related papers  (2022-02-12T15:12:17Z)
• Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency [15.04034188283642]
  Local SGD is a promising approach to overcome the communication overhead in distributed learning. We show that local SGDA can provably optimize distributed minimax problems in both homogeneous and heterogeneous data.
  arXiv  Detail & Related papers  (2021-02-25T20:15:18Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Minibatch vs Local SGD for Heterogeneous Distributed Learning [28.80878557506603]
  We argue that Minibatch SGD dominates all existing analysis of Local SGD in this setting. We present the first upper bound for Local SGD that improves over Minibatch SGD in a non-homogeneous regime.
  arXiv  Detail & Related papers  (2020-06-08T16:40:49Z)
• A Unified Theory of Decentralized SGD with Changing Topology and Local Updates [70.9701218475002]
  We introduce a unified convergence analysis of decentralized communication methods. We derive universal convergence rates for several applications. Our proofs rely on weak assumptions.
  arXiv  Detail & Related papers  (2020-03-23T17:49:15Z)
• Is Local SGD Better than Minibatch SGD? [60.42437186984968]
  We show how all existing error guarantees in the convex setting are dominated by a simple baseline, minibatch SGD. We show that indeed local SGD does not dominate minibatch SGD by presenting a lower bound on the performance of local SGD that is worse than the minibatch SGD guarantee.
  arXiv  Detail & Related papers  (2020-02-18T19:22:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.