Performance Evaluation of Federated Learning Techniques for Predicting Irrigation Water Requirements of Farms

Accurate detection and prediction of required water for irrigating a farm plays an important role in both the quality and quantity of its products. Machine learning is considered as an appropriate solution to detect and predict the required water of a farm if enough and proper data can be provided. To this end, the farms of a region need to share their data. However, many farms because of privacy issues may not want to share their data with others. In our previous work, we proposed Federated Learning (FL) as a solution. While FL can provide an accurate prediction for the required water of farms, it ensures data privacy. In this paper, we implement FL for predicting the required water of a farm. We compare the performance of four different versions of FL (i.e., FedAvg, FL with feedback, FL with influence and FL with feedback and influence) to find which one is more appropriate for the application of predicting the required water of a farm. The results show that FL with influence can provide the best performance.