Multiuser Semantic Communication With Federated Learning for Intelligent Search Service

Intelligent search enables users to access information from the Internet quickly, but existing schemes fail to achieve accurate semantic awareness and reliable information transmission, especially in constrained communication conditions, which degrade search accuracy and personalized user experience. To address these challenges, we propose a multiuser semantic communication system to perform personalized search (PS) tasks, named MU-SemCom-PS. In particular, the system introduces a novel semantic encoder at the transmitter to deeply extract user-specific search semantics by analyzing search history from multiple perspectives, and designs a semantic decoder at the receiver to recover and enhance search semantics by leveraging implicit correlations among users, thus the PS tasks are performed based on the recovered search semantics. To optimize the PS tasks for all users, the federated learning (FL) framework is leveraged to jointly train the MU-SemCom-PS system through knowledge collaboration and sharing. Experimental results show that the proposed scheme significantly improves search accuracy and robustness under constrained communication conditions.