Federated machine learning for multi-domain operations at the tactical edge

Cirincione, Greg; Verma, Dinesh

doi:10.1117/12.2526661

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…To help make sense of the ongoing situation in a coalition operation, militaries will increasingly rely on AI technologies to obtain insights that can assist human decision makers. [8][9][10] The envisaged scenario poses several challenges for current AI techniques. 11 1.…”

Section: Ai In Coalition Operationsmentioning

confidence: 99%

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Tomsett¹,

Preece

Braines³

et al. 2020

Patterns

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Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations. These include having to deal with limited, low-quality data, which inevitably compromises AI performance. We suggest that these problems can be mitigated by taking steps that allow rapid trust calibration so that decision makers understand the AI system's limitations and likely failures and can calibrate their trust in its outputs appropriately. We propose that AI services can achieve this by being both interpretable and uncertainty-aware. Creating such AI systems poses various technical and human factors challenges. We review these challenges and recommend directions for future research.

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Section: Ai In Coalition Operationsmentioning

confidence: 99%

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Tomsett¹,

Preece

Braines³

et al. 2020

Patterns

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“…Federated machine learning is a collaborative training approach where training data is not exchanged to the edge device so as to overcome constraints on training data sharing (policy, security, and coalition constraints) and insufficient network capacity. To address the aforementioned issues, G. Cirincione and D. Verma [13] proposed a federated machine learning approach for deployment of MDO.…”

Section: Defence/ Militarymentioning

confidence: 99%

Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities

Victor¹,

Rajeswari.²,

Alazab³

et al. 2022

Preprint

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Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in machine learning, that will help in fulfilling the challenges faced by conventional ML approaches in IoUT. This paper presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.

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“…The time scale of tasks and actions impacted by these uncertainties needs to be taken into account when deciding on the most appropriate form of modeling. For example, less time-critical tasks with a lower level of uncertainty can benefit from adaptive automation of converting learning models to algorithms, processes and workflows, while more time-critical ones with greater sensitivity require acceleration techniques and continuous incremental learning [44].…”

Section: Open Challenges and Future Workmentioning

confidence: 99%

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

Furdek

Natalino

Giglio

et al. 2020

J. Opt. Commun. Netw.

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As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating Machine Learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing a novel functional block called Security Operation Center (SOC), describing its architecture, specifying key requirements on the supported functionalities and providing guidelines on its integration with optical layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between ML accuracy and run time complexity.

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Federated machine learning for multi-domain operations at the tactical edge

Cited by 20 publications

References 9 publications

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities

Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

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