Scaling-up Distributed Processing of Data Streams for Machine Learning

• URL: http://arxiv.org/abs/2005.08854v2
• Date: Mon, 31 Aug 2020 23:48:59 GMT
• Title: Scaling-up Distributed Processing of Data Streams for Machine Learning
• Authors: Matthew Nokleby, Haroon Raja, and Waheed U. Bajwa
• Abstract summary: This paper reviews recently developed methods that focus on large-scale distributed optimization in the compute- and bandwidth-limited regime. It focuses on methods that solve: (i) distributed convex problems, and (ii) distributed principal component analysis, which is a non problem with geometric structure that permits global convergence.
• Score: 10.581140430698103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the learned models is essential for improved inference in these applications. Further, these applications often involve data that are either inherently gathered at geographically distributed entities or that are intentionally distributed across multiple machines for memory, computational, and/or privacy reasons. Training of models in this distributed, streaming setting requires solving stochastic optimization problems in a collaborative manner over communication links between the physical entities. When the streaming data rate is high compared to the processing capabilities of compute nodes and/or the rate of the communications links, this poses a challenging question: how can one best leverage the incoming data for distributed training under constraints on computing capabilities and/or communications rate? A large body of research has emerged in recent decades to tackle this and related problems. This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates. In particular, it focuses on methods that solve: (i) distributed stochastic convex problems, and (ii) distributed principal component analysis, which is a nonconvex problem with geometric structure that permits global convergence. For such methods, the paper discusses recent advances in terms of distributed algorithmic designs when faced with high-rate streaming data. Further, it reviews guarantees underlying these methods, which show there exist regimes in which systems can learn from distributed, streaming data at order-optimal rates.

Related papers

• High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates [50.406127962933915]
  We develop solutions to problems which enable us to learn a communication-efficient distributed logistic regression model. In our experiments we demonstrate a large improvement in accuracy over distributed algorithms with only a few distributed update steps needed.
  arXiv  Detail & Related papers  (2024-07-08T19:34:39Z)
• Optimal Data Splitting in Distributed Optimization for Machine Learning [85.99744701008802]
  This study focuses on an optimal ratio of distributed data between the server and local machines for any costs of communications and local computations. The running times of the network are compared between uniform and optimal distributions. The superior theoretical performance of our solutions is experimentally validated.
  arXiv  Detail & Related papers  (2024-01-15T16:30:12Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Online Distributed Learning with Quantized Finite-Time Coordination [0.4910937238451484]
  In our setting a set of agents need to cooperatively train a learning model from streaming data. We propose a distributed algorithm that relies on a quantized, finite-time coordination protocol. We analyze the performance of the proposed algorithm in terms of the mean distance from the online solution.
  arXiv  Detail & Related papers  (2023-07-13T08:36:15Z)
• Towards a Better Theoretical Understanding of Independent Subnetwork Training [56.24689348875711]
  We take a closer theoretical look at Independent Subnetwork Training (IST) IST is a recently proposed and highly effective technique for solving the aforementioned problems. We identify fundamental differences between IST and alternative approaches, such as distributed methods with compressed communication.
  arXiv  Detail & Related papers  (2023-06-28T18:14:22Z)
• A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing [5.404315085380945]
  We present an original communication-constrained Newton-type (NT) algorithm designed to accelerate Federated Learning (FL) The proposed solution is thoroughly validated on real datasets.
  arXiv  Detail & Related papers  (2022-02-11T17:52:56Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• Distributed Estimation of Sparse Inverse Covariance Matrices [0.7832189413179361]
  We propose a distributed sparse inverse covariance algorithm to learn the network structure in real-time from data collected by distributed agents. Our approach is built on an online graphical alternating minimization algorithm, augmented with a consensus term that allows agents to learn the desired structure cooperatively.
  arXiv  Detail & Related papers  (2021-09-24T15:26:41Z)
• Communication-Efficient Distributed Deep Learning: A Comprehensive Survey [22.42450750097714]
  We provide a comprehensive survey of the communication-efficient distributed training algorithms. We first propose a taxonomy of data-parallel distributed training algorithms. We then investigate state-of-the-art studies that address problems in these four dimensions.
  arXiv  Detail & Related papers  (2020-03-10T05:42:44Z)
• Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond [73.03743482037378]
  Distributed learning has become a critical direction of the massively connected world envisioned by many. This article discusses four key elements of scalable distributed processing and real-time data computation problems. Practical issues and future research will also be discussed.
  arXiv  Detail & Related papers  (2020-01-14T14:11:32Z)

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.