C2 design considerations for federated AI/ML systems

James Michaelis; Alessandro Morelli; Muddasar Ali

doi:10.1117/12.2618587

6 June 2022 C2 design considerations for federated AI/ML systems

James Michaelis, Alessandro Morelli, Muddasar Ali

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130S (2022) https://doi.org/10.1117/12.2618587
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Building on growth in the commercial IoT space, the Internet of Battlefield Things (IoBT) paradigm envisions mixed application of commercial and military IoT technologies to support Multi-Domain Operations via next-generation Artificial Intelligence and Machine Learning (AI/ML) applications. Operational environments for IoBT are expected to feature significant data volume and variety to support mission needs at varying echelons, while simultaneously requiring coordinated asset management and AI/ML usage across distributed coalitions. These conditions motivate the design and implementation of next-generation infrastructures intended to facilitate: (1) Data exchange between systems according to broader mission needs; (2) Coordinated AI/ML processing across systems; (3) Intelligent prioritization of cross-infrastructure processing and dissemination. Towards achieving these aims, this work looks into the design and prototyping of an AI/ML pipeline, aimed at: (1) Ingest and processing of video feeds; (2) Prioritization of video feed object detections based on Value of Information (VoI) assessment; (3) Support for information exchange with external systems. This submission will cover implementation details of the AI/ML pipeline, a reference usage scenario for IoT data processing and information exchange, and planned future work.

Conference Presentation

Citation Download Citation

James Michaelis, Alessandro Morelli, and Muddasar Ali "C2 design considerations for federated AI/ML systems", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130S (6 June 2022); https://doi.org/10.1117/12.2618587

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