This paper studies a distributed machine learning problem by applying a distributed optimization algorithm over an undirected and connected communication network. Each node has its own fuzzy logic system (FLS) based machine whose weights are trained by the proposed FLS-based distributed cooperative learning (DCL) algorithm to reach the optimum of the global cost function. The training process utilizes the data that are distributed among different nodes and cannot be gathered at any node in the network. The main advantages of the FLS-based DCL algorithm are as follows: It has an exponential convergence; it requires a small amount of computation and communication at each iteration step; and the private and confidential information is protected without exchanging raw data between neighboring nodes. These advantages are verified by performing simulation experiments to compare the FLS-based DCL algorithm with the distributed average consensus based learning algorithm, the alternating direction method of multipliers based learning algorithm and the diffusion least-mean square algorithms.

中文翻译：

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通过模糊逻辑系统在网络上进行分布式协作学习：性能分析和比较

本文通过在无向和连接的通信网络上应用分布式优化算法来研究分布式机器学习问题。每个节点都有自己的基于模糊逻辑系统 (FLS) 的机器，其权重通过提出的基于 FLS 的分布式协作学习 (DCL) 算法进行训练，以达到全局成本函数的最优值。训练过程利用分布在不同节点之间并且无法在网络中的任何节点收集的数据。基于 FLS 的 DCL 算法的主要优点如下： 具有指数收敛性；它在每个迭代步骤都需要少量的计算和通信；并且在相邻节点之间不交换原始数据的情况下保护私有和机密信息。