# Distributed support-vector-machine over dynamic balanced directed networks

arXiv: https://arxiv.org/abs/2104.00399 · HTML: https://mlsanity.com/paper/2104.00399/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [On the Convergence of Decentralized Gradient Descent](https://mlsanity.com/paper/1310.7063/) (2013)
- [Time-Varying Convex Optimization: Time-Structured Algorithms and Applications](https://mlsanity.com/paper/2006.08500/) (2020)
- [A general framework for decentralized optimization with first-order methods](https://mlsanity.com/paper/2009.05837/) (2020)
- [Distributed Convex Optimization for Continuous-Time Dynamics with Time-Varying Cost Function](https://mlsanity.com/paper/1507.04878/) (2015)
- [Single-Bit Consensus with Finite-Time Convergence: Theory and Applications](https://mlsanity.com/paper/2001.00141/) (2020)
- [A Fast Distributed Asynchronous Newton-Based Optimization Algorithm](https://mlsanity.com/paper/1901.01872/) (2019)

## Built on by (most cited first, 9 of 9)

- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [Discretized Distributed Optimization over Dynamic Digraphs](https://mlsanity.com/paper/2311.07939/) (2023)
- [Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data](https://mlsanity.com/paper/2406.00621/) (2024)
- [Distributed saddle point problems for strongly concave-convex functions](https://mlsanity.com/paper/2202.05812/) (2022)
- [Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks](https://mlsanity.com/paper/2410.20345/) (2024)
- [Distributed Finite-Sum Constrained Optimization subject to Nonlinearity on the Node Dynamics](https://mlsanity.com/paper/2203.14527/) (2022)
- [D-SVM over Networked Systems with Non-Ideal Linking Conditions](https://mlsanity.com/paper/2304.06667/) (2023)
- [Fast networked data selection via distributed smoothed quantile estimation](https://mlsanity.com/paper/2406.01929/) (2024)
- [Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap](https://mlsanity.com/paper/2608.01220/) (2026)

## Abstract (arXiv)

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.