| Abstract |
The Army is evolving its war fighting concepts to militarily compete, penetrate, dis-integrate, and exploit adversaries as part of a Multi-Domain Operations (MDO) Joint Force. Arti ficial Intelligence/Machine Learning (AI/ML) is critical to the Armys vision for AI-enabled capabilities to achieve MDO but has signifi cant challenges and risks. The Army faces rapidly changing, never-before-seen situations, where pre-existing training data will quickly become ineffective; tactical training data is noisy, incomplete, and erroneous; and adversaries will employ deception. This is especially challenging at the Tactical Edge that operates in complex urban settings that are dynamic, distributed, resource-constrained, fast-paced, contested, and often physically and virtually isolated. Federated machine learning is collaborative training where training data is not exchanged in order to overcome constraints on training data sharing (policy, security, coalition constraints) and/or insufficient network capacity that are prevalent at the Tactical Edge. We describe the applicability of federated machine learning to MDO using a motivating scenario and identify when it is advantageous to be used. The attributes and design inputs for the deployment of AI/ML (learn-infer-act process), the factors that impact learning-inference processes, and the operational factors impacting the deployment of machine learning are identi fied. We propose strategies for six AI/ML deployment regimes that are at the intersection of total uncertainty (model and environmental) and the operational timeliness that is required, and map AI/ML techniques to address these challenges and requirements. Scientifi c research questions that must be answered to ll critical knowledge gaps are identi fied, and ongoing research approaches to answer them are highlighted. |