Distributed support-vector-machine over dynamic balanced directed networks

• URL: http://arxiv.org/abs/2104.00399v1
• Date: Thu, 1 Apr 2021 11:02:10 GMT
• Title: Distributed support-vector-machine over dynamic balanced directed networks
• Authors: Mohammadreza Doostmohammadian, Alireza Aghasi, Themistoklis Charalambous, and Usman A. Khan
• Abstract summary: We consider the binary classification problem via distributed Support-Machines. We propose a continuous-time algorithm that incorporates network topology changes in discrete jumps.
• Score: 10.76210145983805
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we consider the binary classification problem via distributed Support-Vector-Machines (SVM), where the idea is to train a network of agents, with limited share of data, to cooperatively learn the SVM classifier for the global database. Agents only share processed information regarding the classifier parameters and the gradient of the local loss functions instead of their raw data. In contrast to the existing work, we propose a continuous-time algorithm that incorporates network topology changes in discrete jumps. This hybrid nature allows us to remove chattering that arises because of the discretization of the underlying CT process. We show that the proposed algorithm converges to the SVM classifier over time-varying weight balanced directed graphs by using arguments from the matrix perturbation theory.

Related papers

• Efficient Distributed Learning over Decentralized Networks with Convoluted Support Vector Machine [2.722434989508884]
  This paper addresses the problem of efficiently classifying high-dimensional data over decentralized networks. We develop an efficient generalized alternating direction method of multipliers (ADMM) algorithm for solving penalized SVM.
  arXiv  Detail & Related papers  (2025-03-10T17:31:26Z)
• Vanishing Variance Problem in Fully Decentralized Neural-Network Systems [0.8212195887472242]
  Federated learning and gossip learning are emerging methodologies designed to mitigate data privacy concerns. Our research introduces a variance-corrected model averaging algorithm. Our simulation results demonstrate that our approach enables gossip learning to achieve convergence efficiency comparable to that of federated learning.
  arXiv  Detail & Related papers  (2024-04-06T12:49:20Z)
• Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation [49.827306773992376]
  Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions. Our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks.
  arXiv  Detail & Related papers  (2023-12-19T15:34:52Z)
• Unsupervised Graph-based Learning Method for Sub-band Allocation in 6G Subnetworks [2.0583251142940377]
  We present an unsupervised approach for frequency sub-band allocation in wireless networks using graph-based learning. We model the subnetwork deployment as a conflict graph and propose an unsupervised learning approach inspired by the graph colouring and the Potts model to optimize the sub-band allocation.
  arXiv  Detail & Related papers  (2023-12-13T12:57:55Z)
• Skew-Symmetric Adjacency Matrices for Clustering Directed Graphs [5.301300942803395]
  Cut-based directed graph (digraph) clustering often focuses on finding dense within-cluster or sparse between-cluster connections. For flow-based clusterings the edges between clusters tend to be oriented in one direction and have been found in migration data, food webs, and trade data.
  arXiv  Detail & Related papers  (2022-03-02T20:07:04Z)
• Improved Dual Correlation Reduction Network [40.792587861237166]
  We propose a novel deep graph clustering algorithm termed Improved Dual Correlation Reduction Network (IDCRN) By approximating the cross-view feature correlation matrix to an identity matrix, we reduce the redundancy between different dimensions of features. We also avoid the collapsed representation caused by the over-smoothing issue in Graph Convolutional Networks (GCNs) through an introduced propagation regularization term.
  arXiv  Detail & Related papers  (2022-02-25T07:48:32Z)
• Decomposing neural networks as mappings of correlation functions [57.52754806616669]
  We study the mapping between probability distributions implemented by a deep feed-forward network. We identify essential statistics in the data, as well as different information representations that can be used by neural networks.
  arXiv  Detail & Related papers  (2022-02-10T09:30:31Z)
• Deep Graph Clustering via Dual Correlation Reduction [37.973072977988494]
  We propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) In our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level. In order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information.
  arXiv  Detail & Related papers  (2021-12-29T04:05:38Z)
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities [125.62877849447729]
  We consider distributed variational inequalities (VIs) on domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We make a very general assumption on the computational network that covers the settings of fully decentralized calculations. We theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone settings.
  arXiv  Detail & Related papers  (2021-06-15T17:45:51Z)
• Gradient Coding with Dynamic Clustering for Straggler-Tolerant Distributed Learning [55.052517095437]
  gradient descent (GD) is widely employed to parallelize the learning task by distributing the dataset across multiple workers. A significant performance bottleneck for the per-iteration completion time in distributed synchronous GD is $straggling$ workers. Coded distributed techniques have been introduced recently to mitigate stragglers and to speed up GD iterations by assigning redundant computations to workers. We propose a novel dynamic GC scheme, which assigns redundant data to workers to acquire the flexibility to choose from among a set of possible codes depending on the past straggling behavior.
  arXiv  Detail & Related papers  (2021-03-01T18:51:29Z)
• Unsupervised Learning for Asynchronous Resource Allocation in Ad-hoc Wireless Networks [122.42812336946756]
  We design an unsupervised learning method based on Aggregation Graph Neural Networks (Agg-GNNs) We capture the asynchrony by modeling the activation pattern as a characteristic of each node and train a policy-based resource allocation method.
  arXiv  Detail & Related papers  (2020-11-05T03:38:36Z)
• Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing [113.52575069030192]
  Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model.
  arXiv  Detail & Related papers  (2020-10-02T10:41:59Z)

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.