A method and system for more efficient federated learning (FL) of a machine learning (ML) model using user equipment (UEs) in cellular networks are disclosed. In particular, a system is provided for reducing the impact of poor channel conditions in a cellular network on the FL process. The cellular network may be a 5G, 6G or next generation cellular network. Advantageously, this disclosure creates redundancies in the transmission of FL trained model parameters to reduce the likelihood of an FL training process being stalled by a failure in transmission of data between UEs and a central parameter server which updates an ML model using data received from UEs.