Federated Learning Architectures: A Performance Evaluation with Crop Yield Prediction Application

TL;DR

This paper evaluates centralized and decentralized federated learning architectures for crop yield prediction using LSTM, demonstrating high accuracy and reduced response time, and compares their performance in IoT-based agricultural applications.

Contribution

It provides a comparative analysis of centralized and decentralized federated learning frameworks specifically applied to crop yield prediction, highlighting their performance benefits.

Findings

Centralized FL achieves ≥97% accuracy.

Decentralized FL achieves >97.5% accuracy.

Centralized FL reduces response time by ~75%.

Abstract

Federated learning has become an emerging technology for data analysis for IoT applications. This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short-Term Memory Network. For centralized federated learning, multiple clients and one server is considered, where the clients exchange their model updates with the server that works as the aggregator to build the global model. For the decentralized framework, a collaborative network is formed among the devices either using ring topology or using mesh topology. In this network, each device receives model updates from the neighbour devices, and performs aggregation to build the upgraded model. The performance of the centralized and decentralized federated learning frameworks are evaluated in terms of prediction accuracy, precision, recall, F1-Score, and training time. The experimental results present that $\geq$97% and $>$97.5% prediction accuracy are achieved using the centralized and decentralized federated learning-based frameworks respectively. The results also show that the using centralized federated learning the response time can be reduced by $\sim$75% than the cloud-only framework. Finally, the future research directions of the use of federated learning in crop yield prediction are explored in this paper.