it-7th/FL_Privacy

FL_Privacy is a student-led academic project from Eötvös Loránd University (ELTE). With zero stars, forks, and social velocity, it lacks any market traction or community adoption. The project serves as a pedagogical exercise rather than a production-grade tool. It operates in a space dominated by heavyweights like OpenMined's PySyft, Google's TensorFlow Federated (TFF), and the Flower framework. These established projects provide significantly more robust primitives for SMPC and DP with massive community backing. The defensibility is near zero as the code is a reimplementation of standard privacy attack/defense patterns used for learning purposes. While privacy in Federated Learning is a critical 'frontier' topic, this specific implementation is unlikely to be utilized outside of its original classroom context. Any technical investor should view this as a reference implementation for educational purposes rather than a viable infrastructure candidate.