TITLE:
Decentralized Elastic-Net Broad Learning System over Directed Graphs
AUTHORS:
Jinyan Liang, Manrui Zhou
KEYWORDS:
Broad Learning System (BLS), Directed Graphs, AB/Push-Pull, Distributed Learning
JOURNAL NAME:
Journal of Computer and Communications,
Vol.14 No.7,
July
27,
2026
ABSTRACT: In this paper, we propose a decentralized version of elastic-net broad learning systems, focusing on the case when directed graphs characterize the communication between agents. This novel proposed algorithm is referred to as Directed-DENBLS, which involves decentralized learning for feature fine-tuning and decentralized learning for output weights. However, considering that in a directed graph, it may not be possible to construct a doubly stochastic matrice in a distributed manner. So the proposed algorithm is built on the AB/Push-Pull algorithm, which creates a row stochastic matrix and a column stochastic matrix to address the resulting problem in a decentralized manner. Simulation results on the datasets demonstrate the effectiveness of the proposed Directed-DENBLS algorithm.