A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
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2 Pith papers cite this work. Polarity classification is still indexing.
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The paper reviews energy-aware computing literature and constructs a taxonomy organized by hardware/software aspects, measurement, optimizations, scheduling, scaling, consolidation, federated learning, and cooling.
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Protecting On-Device AI Inference: A Systematic Review of Attacks and Defence Mechanisms
A systematic review of on-device AI inference security finds defenses are imbalanced, with roughly half focused on IP theft while one-third of attacks (adversarial examples) lack any associated defenses.
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Energy-Aware Computing in the Year 2026
The paper reviews energy-aware computing literature and constructs a taxonomy organized by hardware/software aspects, measurement, optimizations, scheduling, scaling, consolidation, federated learning, and cooling.