This talk introduces novel federated learning architectures designed for edge deployment, addressing critical challenges in privacy-preserving AI systems. We present breakthrough research on soft-embeddings combined with classifier-as-retriever approaches that achieve 96-99% retrieval accuracy while dramatically reducing computational overhead compared to traditional methods. The session covers webFrame, a distributed framework enabling large language model decomposition and training on Apple silicon, and explores practical implementation of differential privacy mechanisms in federated environments. Attendees will gain insights into enterprise-ready solutions for on-device small language models, knowledge graph deployment at the edge, and privacy-preserving RAG systems that maintain data sovereignty while delivering production-grade performance for business applications.