Novel Federated Learning Approach for Privacy-Preserving Data Sovereignty Enforcement in IoT Ecosystems

**Abstract:** This paper introduces a novel Federated Learning (FL) framework, incorporating Differential Privacy (DP) and Homomorphic Encryption (HE), specifically tailored for enforcing data sovereignty regulations within Internet of Things (IoT) deployments. Our approach, Federated Sovereign Learning (FSL), addresses the challenge of ensuring data residency and control while leveraging distributed data for collective intelligence. By combining FL with robust privacy-enhancing technologies, we enable collaborative machine learning without centralized data aggregation, fundamentally respecting user data ownership and legal jurisdictions. This framework facilitates enhanced trust and usability of IoT services within a stringent regulatory environment, offering a pathway to scalable and compliant deployments. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*