Secure Localization using Federated Learning and Blockchain: A Survey

The widespread adoption of interconnected devices within indoor environments creates numerous possibilities for indoor applications, where positioning services play a crucial role. Nonetheless, the emergence of security concerns adds complexity to the task of developing precise positioning systems, which are essential for the majority of these applications. Hence, the fusion of blockchain and Federated Learning offers a promising solution to address security issues. This paper introduces a theoretical survey that integrates blockchain and Federated Learning to enhance the security of indoor localization mechanisms. The evaluation criteria are based on the system model, localization technique employed, Artificial Intelligence methods utilized, security techniques applied, and the chosen performance metrics. Ultimately, we illuminate the open research issues and challenges associated with the integration of blockchain and Federated Learning in indoor localization systems.