Papers with edge devices
Towards Reliable and Practical Phishing Detection (2025.naacl-industry)
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| Challenge: | Existing datasets lack size and diversity, with only 609 voice phishing samples available in Korean and 638 smishing instances available in English. |
| Approach: | They propose to use a Korean dataset to construct a reliable phishing detection system using language models to evaluate the model's in-domain and unseen attack detection performance. |
| Outcome: | The proposed system performs reasonably well in voice and unseen attacks while smishing detection remains challenging. |
C2KD: Cross-layer and Cross-head Knowledge Distillation for Small Language Model-based Recommendation (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) show promise but their size and high inference costs limit deployment on resource-constrained devices. |
| Approach: | They propose a framework to transfer task-relevant knowledge from two complementary dimensions to Large Language Models (LLMs) Large Language models (LLMS) have demonstrated great potential in sequential recommendation tasks . |
| Outcome: | Extensive experiments across diverse model families show that the proposed framework achieves competitive performance compared to LLMs. |
MobiZO: Enabling Efficient LLM Fine-Tuning at the Edge via Inference Engines (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are currently pre-trained and fine-tuned on large cloud servers . fine-timing on resource-constrained edge devices presents significant memory and computational demands . |
| Approach: | They propose a resource-efficient fine-tuning framework for LLMs specifically designed for edge devices. |
| Outcome: | Experiments show that MobiZO achieves substantial runtime speedups and memory savings while improving fine-tuning accuracy. |