A Deployment Method of Multi-user Semantic Communication System Based on Federated Learning

‍ ‍Semantic communication is a new communication technology with development potential. It can reduce the amount of data needed for transmission by mining the semantic information in the source. Semantic communication usually adopts the way of deep learning to establish the encoding and decoding model and realize the end-to-end data transmission on the premise that the transceiver shares the model parameters. However， in the actual scene， due to the existence of multiple users， the end-to-end transmission has limitations， and the deployment of semantic communication system has more problems to be considered. In order to apply semantic communication to multi-user scenarios， this paper proposed a federated learning deployment mode of semantic communication system model， which used the user data to train the deep learning model more effectively. Thus， without using user data directly， the model learned the characteristics of user data， and realized the deployment of semantic communication system in multi-user scenario. The simulation results show that the model obtained by federated learning training can achieve the effect close to centralized training， and protect the user's privacy.