Parallelization of the self-organized maps algorithm for federated learning on distributed sources

Abstract

This paper describes a formally based approach for parallelizing the Kohonen algorithm used for the federated learning process in a special kind of neural networks—Self-Organizing Maps. Our approach enables executing the parallel algorithm version on the distributed data sources, taking into account the kind of data distribution on the nodes. Compared to the traditional approaches, we distinguish two kinds of data distributions—horizontal and vertical: for both, our suggested approach avoids gathering data in a single storage, but rather moves computations nearer to the data source nodes. This reduces the execution time of the algorithm, the network traffic, and the risk of an unauthorized access to the data during their transmission. Our experimental evaluation demonstrates the advantages of the approach.