<![CDATA[The rapid proliferation of Edge AI has introduced significant cybersecurity challenges, including adversarial attacks, model theft, and data privacy concerns. Traditional deep learning models deployed on edge devices often suffer from high computational complexity and memory requirements, making them vulnerable to exploitation. This paper explores the integration of model distillation and quantization techniques to enhance the security and efficiency of Edge AI systems. Model distillation reduces model complexity by transferring knowledge from a large, cumbersome model (teacher) to a compact, efficient one (student), thereby improving resilience against adversarial manipulations. Quantization further optimizes the student model by reducing bit precision, minimizing attack surfaces while maintaining performance. We present a comprehensive analysis of how these techniques mitigate cybersecurity threats such as model inversion, membership inference, and evasion attacks. Additionally, we evaluate trade-offs between model accuracy, latency, and robustness in resource-constrained edge environments. Experimental results on benchmark datasets demonstrate that distilled and quantized models achieve comparable accuracy to their full-precision counterparts while significantly reducing vulnerability to cyber threats. Our findings highlight the potential of distillation and quantization as key enablers for secure, lightweight, and high-performance Edge AI deployments.]]>
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