Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data

Automatic modulation classification (AMC) is a core enabler of cognitive wireless systems, providing spectrum awareness and supporting adaptive communication at the network edge. However, training AMC models on centrally aggregated data incurs high communication overhead, raises privacy concerns, and often lacks robustness to real-world conditions. We propose FedSSL-AMC, a federated self-supervised framework for learning AMC models from sparsely labeled, distributed I/Q time-series data. Participating clients collaboratively train a causal, time-dilated CNN encoder using triplet-loss self-supervision on unlabeled signals, followed by lightweight local SVMs trained on limited labeled samples. This enables communication-round-efficient, robust representation learning under class imbalance and channel variability. We establish convergence guarantees for a proximal variant of the encoder-training procedure and derive a separability bound for the downstream classifier under feature noise. Experiments on synthetic and over-the-air datasets demonstrate improvements over supervised FL baselines across all three datasets and nearly all evaluated settings involving heterogeneous SNRs, carrier-frequency offsets, and non-IID label distributions.