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This is a Systematic Review study.

August 26, 2025Science and Technology of Engineering Chemistry and Environmental ProtectionOpen Access

Toward Real-Time and Efficient Edge Intelligence: Advances and Challenges in Lightweight Machine Learning

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Key achievements include reducing model size and complexity while preserving accuracy, yet challenges remain regarding trade-offs between accuracy and efficiency.
The review emphasizes three technical pillars: model compression, efficient architecture design, and hardware-aware optimization for resource-constrained environments.
Analysis of edge intelligence solutions reveals persistent issues like hardware fragmentation and privacy/security concerns during deployment.
Emerging methodologies such as neural-symbolic learning and adaptive federated learning could significantly enhance edge computing performance and capabilities.

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Implication

This review analyzes lightweight machine learning approaches to improve efficiency and reduce latency in edge devices, highlighting key techniques and challenges.

Cite This Study

Xiang Gao (2025) studied this question.

synapsesocial.com/papers/68af63e9ad7bf08b1eae4723 https://doi.org/10.61173/bwn0ez36

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Implication

This review analyzes lightweight machine learning approaches to improve efficiency and reduce latency in edge devices, highlighting key techniques and challenges.

Toward Real-Time and Efficient Edge Intelligence: Advances and Challenges in Lightweight Machine Learning

Key Points

Discussion

Implication

Cite This Study

Toward Real-Time and Efficient Edge Intelligence: Advances and Challenges in Lightweight Machine Learning

Key Points

Discussion

Implication

Cite This Study

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