Communication-efficient model pruning for federated learning in mobile edge computing
In the mobile edge computing scenario, the distributed architecture of federated learning allows the edge server and mobile terminals to cooperatively train the deep model, without necessitating sharing of local data across the mobile terminals.While the training process generally consists of multiple rounds between the server and several clients,