Semantic Feature-based Learning Framework for Energy-Limited Semantic Communication System

In this paper, we investigate a distributed semantic communication system where each device utilizes the semantic model for intelligent tasks. Due to the challenges associated with heterogeneity of federated learning framework, we propose a semantic feature-based learning framework (SFLF) for task-oriented semantic communication. Additionally, we define the global task score to evaluate the task execution performance of whole devices covered by the edge server and formulate a global task score maximization problem under energy constraints. For such framework, the edge server aggregates the semantic features extracted from devices and broadcasts global semantic feature to devices. The devices utilize their local data and the received global semantic feature to train their semantic models by the task loss function and semantic loss function. Besides, since the practical distributed system is usually energy-limited, we propose a distributed proximal policy optimization (DPPO)based scheduling algorithm to adjust energy consumption in real-time during the training process. Simulation results demonstrate that the proposed SFLF with the DPPO-based device scheduling algorithm outperforms the existing schemes.