联邦自监督学习解决无线调制识别中的数据异构与标签稀疏问题。
Federated Self-Supervised Modulation Classification under Non-IID and Imbalanced Data
- 通过自监督对比损失在无标签信号上训练时延卷积编码器。
- 在低标签数据下实现比传统联邦学习更高的分类准确率。
- 适合边缘计算中隐私敏感、数据分布不均的通信场景。
自动调制分类（AMC）是认知无线系统的核心，可提供频谱感知并支持网络边缘的自适应通信。然而，在集中聚合数据上训练AMC模型会带来高通信开销、隐私风险，并且对真实环境下的鲁棒性不足。我们提出FedSSL-AMC，一种基于稀疏标注、分布式I/Q时序数据的联邦自监督框架。参与客户端通过三元组损失在无标签信号上协作训练因果时延卷积编码器，随后使用少量标注样本训练轻量级局部SVM分类器。该方法在类别不平衡和信道变化条件下实现了通信轮次高效、鲁棒的表征学习。我们为编码器训练过程的近端变体建立了收敛性保证，并推导了在特征噪声下下游分类器的可分性边界。在合成数据集和实测数据集上的实验表明，该方法在所有三个数据集及几乎所有评估设置（包括异构信噪比、载波频偏和非独立同分布标签分布）下均优于监督式联邦学习基线。
原文摘要 · Abstract (English)
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.
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