Deep Fingerprinting Data Learning Based on Federated Differential Privacy for Resource-Constrained Intelligent IoT Systems

With the rapid integration of Internet of Things (IoT) devices and artificial intelligence (AI) function, the data management and privacy issue has drawn great attentions in intelligent IoT systems where communication infrastructures frequently exchange open data flows over the air. Therefore, lightweight and private access over radio communication pipes becomes a critical but challengeable need for resource-constrained IoT devices due to the limited memory capacity, computing, and energy consumption. In this paper, we develop the concept of deep federated scattering fingerprinting aided by differential privacy (DFSF-DP) in which a deep fingerprinting data learning network exploits fingerprinting data to realize lightweight intelligent access and incorporates federated learning with differential privacy to guarantee the data privacy in a way of distributed training. Particularly, first, we employ a wavelet scattering network for the efficient radio frequency fingerprinting (RFF) feature extraction and construct a high information density database. Subsequently, the implementation of distributed learning minimizes the demand for computing resources, by exploiting the full potential of edge and cloud nodes to aggregate the global model. To bolster the data privacy and security, adaptive clipping and gradient noising are incorporated into DFSF-DP. Experimental results demonstrate that DFSFDP obtains outstanding performance and achieves equivalent advancements while utilizing a mere 25% of the original dataset. Moreover, it attains a 93% identification accuracy with 0.1 noise multiplier which confirms the remarkable performance of DFSFDP while upholding privacy and security considerations.