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

Abstract

Introduction: Federated learning has become an emerging technology in data analysis for IoT applications. Methods: This paper implements centralized and decentralized federated learning frameworks for crop yield prediction based on Long Short-Term Memory Network and Gated Recurrent Unit. For centralized federated learning, multiple clients and one server are 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 neighboring..