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Khatana, V. and Salapaka, M.V. (2020) D-DistADMM: A O(1/k) Distributed ADMM for Distributed Optimization in Directed Graph Topologies. 2020 59th IEEE Conference on Decision and Control (CDC), Jeju Island, 14-18 December 2020, 2992-2997.
https://doi.org/10.1109/cdc42340.2020.9304086

has been cited by the following article:

• 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.