Ongoing research with The International Technology Alliance in Distributed Analytics and Information Sciences (DAIS-ITA) aims to enable secure, dynamic, semantically-aware, distributed analytics for deriving situational understanding in future coalitions. This paper sets out an example military scenario and operations in a future time frame, to capture the expected battlespace context and key future challenges. Key considerations involve complex multi-actor situations, high complexity information, high tempo processing, all within human-machine hybrid-teams. The coalition composition of these teams is critical and all resources will be constrained. A phased operation is proposed across rural and urban operation involving a range of ISR sensors and autonomous devices. All these are subject to enemy action and perturbation and must be used across a highly contested and congested electromagnetic spectrum. Agile command and control is required across the coalition with information arriving from multiple sources and partners that may also be utilised for learning.
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.
A wide array of military and commercial applications rely on the collection and processing of audio data. One approach to perform analytics and machine learning on such data is to upload and process them at a central server (e.g., cloud) which offers abundant processing resources and the ability to run sophisticated machine learning models and analytics on the audio data. This approach can be inefficient due to the low bandwidth and energy limitations of mobile devices as well as intermittent connectivity to a central collection point such as the cloud. It is also problematic as audio data are often highly sensitive and subject to privacy constraints. An alternative approach is to perform audio analytics at edge of the network where data is generated. The challenge in this approach is the requirement to perform analytics subject to resource constraints which limit performance and accuracy of predictive analytics. In this paper, we present a system for performing predictive analytics on audio data, where the training is executed on the cloud and the classification can be executed at the edge. We present the design principles and architecture of the system, and quantify the performance tradeoff of executing analytics at contemporary edge devices versus the cloud.
The Management of Information Processing Services (MIPS) project has two main objectives; the notification to
analysts of the arrival of relevant new information and the automatic processing of the new information. Within these
objectives a number of significant challenges were addressed. To achieve the first objective, the team had to demonstrate
the capability for specific analysts to be “tipped-off” in real-time that textual reports and sensor-data have been received
that are relevant to their analytical tasks, including the possibility that such reports have been made available by other
nations. In the case of the second objective, the team had to demonstrate the capability for the infrastructure to
automatically initiate processing of input data as it arrives, consistent with satisfying the analytical goals of teams of
analysts, in as an efficient a manner as possible (including the case where data is made available by more than one
nation). Using the Information Fabric middleware developed as part of the International Technology Alliance (ITA)
research program, the team created a service based information processing infrastructure to achieve the objectives and
challenges set by the customer. The infrastructure allows existing software to be wrapped as a service and/or specially
written services to be integrated with each other as well as with other ITA technologies such as the Controlled English
(CE) Store or the Gaian Database. This paper will identify the difficulties in designing and implementing the MIPS
infrastructure together with describing its architecture and illustrating its use with a worked example use case.