Edge-local LLM agent deployments on IoT eliminate routine cloud data exposure but degrade sovereignty during fallbacks and create exploitable failover windows, making architecture a primary security determinant.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A federated framework uses per-client nonlinear state space models and a central graph attention network on latent states to learn and interpret cross-client temporal dynamics with convergence guarantees.
citing papers explorer
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Systems-Level Attack Surface of Edge Agent Deployments on IoT
Edge-local LLM agent deployments on IoT eliminate routine cloud data exposure but degrade sovereignty during fallbacks and create exploitable failover windows, making architecture a primary security determinant.
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Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
A federated framework uses per-client nonlinear state space models and a central graph attention network on latent states to learn and interpret cross-client temporal dynamics with convergence guarantees.