AI RESEARCH
Secure and Parallel Determinant Computation for Large-Scale Matrices in Edge Environments
arXiv CS.AI
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ArXi:2605.22039v1 Announce Type: cross The advent of edge computing has enabled resource-constrained clients to delegate intensive computational tasks to distributed edge servers, especially within Internet of Things (IoT) environments. Among such tasks, Matrix Determinant Computation (MDC) remains critical for applications in control systems, cryptography, and machine learning. However, the cubic complexity of traditional determinant algorithms makes them unsuitable for real-time processing in constrained edge scenarios.