In tactical military networks, decisions must often be made quickly based on information at hand. It is a challenge to provide decision makers with a notion of the quality of the information they have, or to provide a method by which decision makers can specify a required quality of information. It is a further challenge to honor requests for a required quality of information when selecting information sources, transporting information through a highly-dynamic network, and perhaps performing processing on that information. In this paper we motivate the need for a general, but formal, definition of qualityof-information so that this metric may be specified and potentially optimized by algorithms that operate a tactical network. Furthermore, we define a new notion, the operational information content capacity, to capture the amount and quality of information that a network can deliver.
A Federated Learning approach consists of creating an AI model from multiple data sources, without moving large amounts of data across to a central environment. Federated learning can be very useful in a tactical coalition environment, where data can be collected individually by each of the coalition partners, but network connectivity is inadequate to move the data to a central environment. However, such data collected is often dirty and imperfect. The data can be imbalanced, and in some cases, some classes can be completely missing from some coalition partners. Under these conditions, traditional approaches for federated learning can result in models that are highly inaccurate. In this paper, we propose approaches that can result in good machine learning models even in the environments where the data may be highly skewed, and study their performance under different environments.
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