The growth of distributed, institutionally siloed data has created demand for analytics frameworks that ensure privacy, interpretability, and real-time decision support. While federated learning enables decentralized model training without raw data sharing, most existing approaches rely on opaque neural models and lack explicit reasoning capabilities. This paper proposes a Federated Neuro-Symbolic Intelligence (FNSI) framework that integrates federated learning with symbolic reasoning to address these limitations. The architecture combines local neural learning with symbolic constraint enforcement at the client level and a privacy-preserving coordination layer that aggregates encrypted updat... More >