On-Device AI Models: Advancing Privacy-First Machine Learning for Mobile Applications
DOI:
https://doi.org/10.32628/CSEIT2410612397Keywords:
On-Device AI, Privacy-First Computing, Model Compression, Hardware Acceleration, Mobile Edge ComputingAbstract
A revolutionary approach to mobile computing, on-device AI models solve important issues with privacy, latency, and network dependence. The development and optimization of lightweight AI models tailored for mobile devices are examined in this thorough article, which also looks at the delicate balance between user privacy and computing performance. The article looks into several topics, such as performance optimization tactics, effective layer design, privacy enhancement via local processing, and model compression techniques. To enable advanced AI capabilities on devices with limited resources, it also explores implementation strategies and hardware acceleration techniques. This article shows how on-device AI is transforming mobile applications in the social media, healthcare, and financial industries while upholding strong privacy assurances by examining current trends and potential future directions.
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