Global Grids – Making a Case for Self-organization in Large-Scale Overlay Networks

Eymann, Torsten; Streitberger, Werner; Hudert, Sebastian

doi:10.1007/978-3-540-75336-0_4

Cited by 1 publication

(1 citation statement)

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“…The term Gossip Learning 4,5 initially referred to a gossip-based math model to make stock market price predictions. Since then, gossip learning has been used in a variety of ML use cases including a generic classification problem for model convergence over peer-to-peer networks.…”

Section: Related Workmentioning

confidence: 99%

End-to-end trustworthy ML for multidomain operations

Karim

Rawat

2023

Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications V

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In this article, we present SFMLOps, a Security Framework for Machine Learning Operations (MLOps), a comprehensive and novel approach to securing MLOps pipelines in multi-domain operations. SFMLOps can be used to benchmark security in mobile cyber-physical systems like quadruped reconnaissance robots, unmanned autonomous vehicles, and wearable brain-computer interfaces. Our framework examines and categorizes potential attack surfaces and threats within MLOps, offering countermeasures and their effectiveness for various aspects such as data storage, communication channels, model training, and model predictions. We provide security engineers practical guidance on developing secure MLOps pipelines. We introduced a secure pipeline design, MLPipeSec, based on a publisher-subscriber model and implemented on the JointForceNet+ Blockchain to ensure end-to-end trustworthiness across the MLOps pipeline. To evaluate the impact of security parameters on a multi-domain computer vision task, we compared several frameworks for their security and performance using the CIFAR-10 dataset. We also investigated Gossip Learning as a federated learning framework in conjunction with Google Cloud Platform and introduced a new federated learning model, VizFedML. Our experimental results demonstrate the efficacy of the SFMLOps framework and the MLPipeSec design in mitigating a range of vulnerabilities and weaknesses associated with MLOps, contributing to the development of more secure and robust machine learning systems.

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Section: Related Workmentioning

confidence: 99%