Abstract
Modulation recognition technology, as one of the core technologies in the field of wireless communications, holds significant importance in intelligent communication systems such as link adaptation and IoT devices. In recent years, deep learning-based automatic modulation recognition (DL-AMR) has emerged as a major research direction in this domain. Existing DL-AMR schemes primarily adopt a centralized training architecture, where a unified model is trained on a central server using local data from terminal devices. Although such methods achieve high recognition accuracy, they carry substantial privacy leakage risks. Moreover, when terminal devices independently train models solely based on their local data, the model performance often suffers due to issues like data distribution disparities and insufficient training samples. To address the critical challenges of high data privacy leakage risks, excessive communication overhead, and data silos in automatic modulation recognition tasks, this paper proposes a federated automatic modulation recognition method based on characteristic feature fine-tuning (FedeAMR-CFF). Specifically, the clients extract representative features through distance-based metric screening, and the server aggregates model parameters via the FedAvg algorithm and fine-tunes the model using the collected features. This method not only safeguards client data privacy but also facilitates effective knowledge transfer across distributed datasets while significantly mitigating the non-independent and identically distributed problem. Experimental validation demonstrates that FedeAMR-CFF achieves an improvement of 3.43% compared to the best-performing local model.