Distributed Modulation Recognition for IoT Devices in Data-Limited Applications

Deep learning (DL) has been widely utilized in automatic modulation classification (AMC), and its performance depends largely on the presence of high-quality datasets. Motivated by this fact, this work addresses the AMC challenges in data-limited IoT environments, proposing a framework combining few-shot meta-learning and federated learning for resource-constrained devices, where edge nodes use meta-learning for training with global updates via federated averaging (FedAvg). The system aggregates samples from multiple nodes while still maintaining data security. Simulations involved 11 modulation types with varying SNRs, 100 client nodes, and 10 rounds of federated learning. The iterative process includes loading pre-trained parameters, performing local training, averaging local parameters, and updating global parameters. The obtained results show 70% post-training testing accuracy, with a consistently good performance during federated iterations. The results demonstrated the effectiveness of the proposed framework in data-scarce IoT scenarios, offering a robust performance across varying signal qualities while minimizing energy consumption and communication overhead, which is crucial for IoT device longevity and network scalability, highlighting framework’s potential for real-world applications in distributed modulation recognition.