Building secure and scalable privacy-enhanced federated learning systems involves creating architectures where multiple entities collaboratively train models while keeping their data localized, thus enhancing privacy. Such systems employ advanced cryptographic methods like homomorphic encryption and differential privacy to ensure that individual data contributions remain confidential. Scalability is addressed by optimizing communication protocols and data compression techniques to handle large volumes of data efficiently across diverse networks.