Federated Learning (FL) is a distributed machine learning approach that enables model training across decentralized servers without transferring data, thereby maintaining data privacy. It is utilized in various fields, such as healthcare and predictive text technology, to enhance data collaboration without breaching privacy laws. FL can be implemented through different communication patterns and data partitioning methods, with considerations for hardware variations. However, standard FL poses certain risks, including potential data leakage and exposure of proprietary models. To address these issues, Duality Technologies introduces Secure Federated Learning (SFL), which incorporates cryptographic encryption techniques like fully homomorphic encryption and secure multiparty computation to safeguard data and models during training. This enhanced security measure allows for privacy-preserving machine learning, making SFL more secure than traditional FL frameworks while ensuring practical application without requiring users to have cryptographic expertise.