A Study on Federated Learning for Efficient Communication Resource Allocation in 6G Heterogeneous Networks

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The main result was a protocol for managing data processing from multiple sensors. It determines, based on the location of the source sensor and the state of the network, whether to collect and process the data using Low Earth Orbit satellites, or use a central server. Such protocol is useful for assisting collection and training of Distributed and Federated Learning models in heterogeneous networks, increasing training efficiency by 60% . Additionally, there were results on satellite beamforming, task offloading in cloud networks, and digital twins for network logging andanalysis for a total of 5 journal papers and 3 international conferences.