  Semantic communication has emerged as a pillar for the next generation of
communication systems due to its capabilities in alleviating data redundancy.
Most semantic communication systems are built upon advanced deep learning
models whose training performance heavily relies on data availability. Existing
studies often make unrealistic assumptions of a readily accessible data source,
where in practice, data is mainly created on the client side. Due to privacy
and security concerns, the transmission of data is restricted, which is
necessary for conventional centralized training schemes. To address this
challenge, we explore semantic communication in a federated learning (FL)
setting that utilizes client data without leaking privacy. Additionally, we
design our system to tackle the communication overhead by reducing the quantity
of information delivered in each global round. In this way, we can save
significant bandwidth for resource-limited devices and reduce overall network
traffic. Finally, we introduce a mechanism to aggregate the global model from
clients, called FedLol. Extensive simulation results demonstrate the
effectiveness of our proposed technique compared to baseline methods.
