A Service-Based Joint Model Used for Distributed Learning: Application for Smart Agriculture

Distributed analytics facilitate to make the data-driven services smarter for a wider range of applications in many domains, including agriculture. The key to producing services at such level is timely analysis for deriving insights from reliable data. Centralized data analytic services are becoming infeasible due to limitations in the Information and Communication Technologies (ICT) infrastructure, timeliness of the information, and data ownership. Distributed Machine Learning (DML) platforms facilitate efficient data analysis and overcome such limitations effectively. Federated Learning (FL) is a DML methodology, which enables optimizing resource consumption while performing privacy-preserved timely analytics. In order to create such services through FL, there needs to be innovative machine learning (ML) models as data complexity as well as application requirements limit the applicability of existing ML models. Even though NN-based models are highly advantageous, use of NN in FL settings is limited with thin clients (with less computational capabilities) and high-dimensional data (with large number of model parameters). Therefore, in this paper, we propose a novel Neural Network (NN)- and Partial Least Square (PLS) regression- based joint FL model (FL-NNPLS). Its predictive performance is evaluated under sequential- and parallel-updating based FL algorithms in a smart farming context for milk quality analysis. Smart farming is a fast-growing industrial sector which requires effective analytics platforms to enable sustainable farming practices. However, the use of advanced ML techniques are still at a early stage for improving the effectiveness of farming practices. Our FL-NNPLS approach performs and compares well with a centralized approach and demonstrates state-of-the-art performance.