Finally we propose FedPut, utilizing Federated Learning (FL) to develop the throughput prediction model. With the help of real network data, we show in this paper that the existing models get invalidated when trained with the data from one network and then applied to a different network. Precisely, these models are not transferable across networks of different operators because of the vast variability in the underlying network configurations across operators, device specifications, and devices' responsiveness towards an operator's network. However existing works are limited in scope as the prediction models are primarily trained using the data from a closed network under a fixed cellular operator. Downlink throughput prediction using machine or deep learning techniques is one of the significant aspects in this direction. Recent advancement in the ultra-highspeed cellular networks has pivoted the development of various next-generation applications, demanding precise monitoring of the underlying network conditions to adapt their configurations for the best user experience.

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