Smart Device

Smart devices are increasingly leveraging advanced machine learning models to perform complex tasks efficiently on resource-constrained hardware. Current research focuses on optimizing model architectures (like CNNs, LLMs, and transformers) through techniques such as pruning, quantization, and federated learning to reduce computational demands and improve energy efficiency while maintaining accuracy. This work is significant for enabling the deployment of powerful AI capabilities in a wide range of applications, from personalized healthcare and environmental monitoring to enhanced user experiences in consumer electronics. The development of efficient, privacy-preserving algorithms is a key driver of this research.