IMPLEMENTING FEDERATED LEARNING FOR BINARY CLASSIFICATION SOLUTIONS

Abstract

This paper presents a solution that employs federated learning to address binary classification problems. The theoretical foundations of distributed systems and federated learning are primarily analyzed, followed by the implementation of a system for federated neural network training. The developed system enables the allocation and aggregation of model weights across multiple distributed nodes, thereby eliminating risks associated with centralized data storage. The paper includes a review of the employed technologies, a description of the federated system's structure, and an evaluation of the model. This approach significantly contributes to the understanding of federated learning and can serve as a foundation for further research and the development of more advanced distributed systems.