Federated Knowledge Distillation Enabled Image Semantic Communication
Xinmian Xu, Kaipeng Zheng, Haie Dou, Mingkai Chen, Lei Wang · 2024
The transition from 5G to beyond 5G (B5G) heralds a shift towards more pervasive and intelligent communication trends. This evolution necessitates a departure from traditional information theory towards semantic communication (SemCom), propelled by artificial intelligence (AI), aimed at enhancing capacity and optimizing resources. Concurrently, image SemCom (ISC) emerges to empower image applications. However, ISC demands suitable devices and ample computing resources to support complex neural network models and their training, presenting a significant challenge. In response, we propose a federated semantic feature distillation (FedSFD) architecture to enhance the overall performance of ISC. Combining federated learning (FL) and feature distillation (FD), FedSFD facilitates the transfer of group feature knowledge. Specifically, the powerful server model refines itself by approximating the distance between its middle layer features and those of the devices via FD. Subsequently, lightweight device ISC models leverage FD to incorporate the server model’s knowledge during training. This iterative process is bolstered by the information bottleneck (IB)-based loss function, enhancing image compression and reconstruction capabilities. Notably, this architecture operates without necessitating a unified model, thereby offering improved privacy protection. Simulation experiments demonstrate that compared to the baseline, our approach can achieve superior integration capability for ISC at the noisy edge.