Privacy and security for 6G networks via Fog/Edge Computing, Blockchain, and Federated Learning: A survey and taxonomy

The upcoming Sixth Generation (6G) of wireless networks builds on decades of advances and research in scientific areas such as Physics, Electronics & Communication, and Computational Systems. 6G will demand not only ultra-high throughput and low latency, but also scalable, privacy-preserving, and trustworthy coordination across distributed systems. Paradigms such as Fog/Edge Computing (FC/EC), Blockchain (BC), and Federated Learning (FL) each offer innovative solutions to different aspects of these requirements. The combined potential of these technologies, and how they can jointly impact 6G and its applications, are still open issues. Therefore, establishing a roadmap to understand their integration and synergy is needed. In this paper, we present a taxonomy-driven survey. We first analyze FC/EC, BC, and FL and their integration for 6G. Then, as our main contribution, we introduce a comprehensive taxonomy that categorizes seven integration levels (i.e., architecture features, functionalities, security and privacy mechanisms, data management strategies, energy efficiency mechanisms, applications and cross-cutting issues), and explore the effective integration of these paradigms by analyzing their unique characteristics, potential synergies, and opportunities to deliver 6G demands. Through this taxonomy, the study emphasizes transformative benefits such as enhanced data security, improved processing efficiency, and streamlined decentralized privacy-preserving mechanisms, which are actual needs of the in-developing 6G networks. Nonetheless, challenges such as scalability and performance issues and regulatory hurdles are also noted. The work also identifies key areas for future research, and current challenges, promoting exploration into the promising potential of combined FC/EC, BC, and FL solutions for 6G.