Shelby Hall Graduate Research Forum Posters

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Description

The rapid evolution of computational ecosystems—ranging from embedded systems and cloud platforms to hybrid and quantum architectures—has introduced new challenges in deploying machine learning (ML) applications. While cloud computing offers scalability, it comes with increased latency and security risks, whereas edge computing, such as FPGA-based systems, provides real-time processing with constrained resources. Hybrid and quantum ecosystems further complicate decision-making, requiring careful trade-offs between performance and security. This research seeks to establish a framework for evaluating ML performance and security risks across these ecosystems, forming the foundation of the Computational Performance And Security System (COMPASS) decision-support tool. The study will systematically investigate key performance indicators—including latency, energy efficiency, and processing power—alongside security concerns such as data privacy, attack vulnerabilities, and system resilience. At this stage, the research focuses on gathering background information, identifying existing gaps, and defining a comparative methodology for analyzing ML deployment trade-offs. The poster will present a literature review, conceptual models, and initial considerations for structuring the COMPASS framework. By addressing these foundational aspects, this work aims to provide a structured approach to optimizing ML performance and security across diverse computing environments.

Publication Date

3-2025

Department

Information Systems & Technology

City

Mobile

Disciplines

Cybersecurity | Information Security | Other Computer Sciences | Programming Languages and Compilers

Establishing a Framework for Evaluating Machine Learning Performance and Security across Computational Ecosystems

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