Multi-Satellite Federated Learning for On-Orbit Radio Frequency Fingerprint Identification

The openness of the space environment renders satellite Internet vulnerable to external malicious attacks. As a typical approach of physical-layer authentication, radio frequency fingerprint (RFF) identification can be exploited to identify wireless user devices by extracting their unique hardware characteristics, thereby becoming a promising solution to strengthen the authentication security of satellite Internet. However, since satellite Internet is expected to provide globally seamless services to massive users, it is not only overhead-costly but also privacy-risky to construct a centralized RFF identification model by sharing wireless signals of all users in a huge dataset for RFF extraction. Fortunately, by collaborative model training among satellites, federated learning (FL) offers a viable solution for building up an on-orbit distributed RFF identification model that does not require sharing the received signals among satellites. Inspired by this fact, this article investigates the multi-satellite FL framework design for on-orbit RFF identification. In specific, we first briefly introduce the fundamentals of RFF identification. Then, following the idea of pioneering work [1], a vanilla multi-satellite FL framework is presented for on-orbit RFF identification, followed by technical challenges of its practical application, including heterogeneous local datasets, non-identical satellite resources, and dynamic link topology. To overcome these challenges, we further propose a hierarchically collaborative multi-satellite FL framework, under which three key techniques, say scheduling management, model aggregation, and model personalization are also investigated in-depth. Finally, we outline the future research directions and challenges.