Task-Oriented Semantic Communication Via Federated Learning

The transition from 5 G to beyond 5 G (B5G) networks is accelerating the development of ubiquitous and intelligent IoT applications. Semantic communication (SemCom) has emerged as a transformative paradigm that prioritizes task-relevant meaning over bit-level accuracy. However, deploying computationally intensive semantic models on resource-constrained end devices presents a major challenge. To address this issue, we propose FedSC, a new task-oriented semantic communication framework that enables end-edge collaborative learning by integrating federated learning with knowledge distillation. FedSC distributes model training between edge servers and end devices, where lightweight terminal models are collaboratively optimized with the assistance of more capable edge nodes, thus ensuring efficient and privacy-preserving semantic communication across heterogeneous environments. The framework adopts a hierarchical learning architecture in which the end and edge models cooperate via dynamic feature alignment, guided by the information bottleneck principle to balance semantic fidelity and compression. To further enhance efficiency and robustness, FedSC incorporates Gumbel-Softmax discretization for bandwidth-efficient semantic representation and introduces an SNR-Adaptive inference mechanism to adapt to fluctuating wireless conditions. Experiments on Raspberry Pi clusters show that FedSC achieves improved performance in image classification and reconstruction tasks while significantly reducing communication overhead compared to existing baseline approaches.