Publication
Multifaceted Applications of Federated Learning: Beyond Neural Networks
Tatjana Legler; Vinit Hegiste; Martin Ruskowski
In: Kosmas Alexopoulos; Sotiris Makris; Panagiotis Stavropoulos (Hrsg.). Advances in Artificial Intelligence in Manufacturing II. European Symposium on Artificial Intelligence in Manufacturing (ESAIM-2024), Cham, Pages 271-278, ISBN 978-3-031-86489-6, Springer Nature Switzerland, 2025.
Abstract
The advent of Federated Learning (FL) has brought about a revolutionary change in the field of machine learning, enabling the decentralised training of models across a multitude of devices while simultaneously maintaining the confidentiality of the data. In contrast to conventional centralized methodologies, FL maintains the localisation of data, with only model updates being shared. This methodology enhances model generalisation and stability without compromising data sovereignty. A variety of machine learning techniques, including support vector machines (SVMs) and decision trees, can be effectively utilised within the context of FL frameworks. SVMs offer efficient solutions for classification tasks with minimal computational overhead, while decision trees provide interpretable models for both classification and regression. This paper explores the application of these methods in FL settings, highlighting their advantages and potential use cases in diverse industries, particularly in manufacturing. Furthermore, it discusses the integration of reinforcement learning with FL, emphasising its potential for enhancing intelligent and adaptable decentralised systems.