  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.
