Federated learning is a machine learning technique where the model is trained across multiple decentralized devices or servers without exchanging data samples, enhancing privacy and security. In the DoD, federated learning enables the development of AI and machine learning models using decentralized data, which is vital for operational security and data privacy. This approach aligns with the DoD's need for advanced analytics while adhering to stringent data protection standards. Federated learning is instrumental in creating robust AI solutions without compromising sensitive information.