Distributed support vector machines over dynamic balanced directed networks
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
IEEE Control Systems Letters
AbstractIn this letter, we consider the binary classification problem via distributed Support Vector Machines (SVMs), 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.
Publisher Copyright: IEEE
Distributed databases, distributed optimization, Heuristic algorithms, Manganese, matrix perturbation theory., Radio frequency, Signal processing algorithms, Support vector machines, Support Vector Machines, Switches
Doostmohammadian , M , Aghasi , A , Charalambous , T & Khan , U A 2022 , ' Distributed support vector machines over dynamic balanced directed networks ' , IEEE Control Systems Letters , vol. 6 , 9446550 , pp. 758-763 . https://doi.org/10.1109/LCSYS.2021.3086388