A Framework for Distributed Semi-supervised Learning Using Single-layer Feedforward Networks

• 作 者: Jin Xie San-Yang Liu Jia-Xi Chen
• 机 构: School of Mathematics and Statistics,Xidian University,Xi’an 710071,China
• 出 处: 机器智能研究（英文）
• 发表日期: 2022年1期
• 关键词: Distributed learning(DL) semi-supervised learning(SSL) manifold regularization(M

This paper aims to propose a framework for manifold regularization(MR)based distributed semi-supervised learning(DSSL)using single layer feed-forward neural network(SLFNN).The proposed framework,denoted as DSSL-SLFNN is based on the SLFNN,MR framework,and distributed optimization strategy.Then,a series of algorithms are derived to solve DSSL problems.In DSSL problems,data consisting of labeled and unlabeled samples are distributed over a communication network,where each node has only access to its own data and can only communicate with its neighbors.In some scenarios,DSSL problems cannot be solved by central-ized algorithms.According to the DSSL-SLFNN framework,each node over the communication network exchanges the initial paramet-ers of the SLFNN with the same basis functions for semi-supervised learning(SSL).All nodes calculate the global optimal coefficients of the SLFNN by using distributed datasets and local updates.During the learning process,each node only exchanges local coefficients with its neighbors rather than raw data.It means that DSSL-SLFNN based algorithms work in a fully distributed fashion and are pri-vacy preserving methods.Finally,several simulations are presented to show the efficiency of the proposed framework and the derived al-gorithms.