Device Training

Device training focuses on adapting and fine-tuning machine learning models directly on resource-constrained devices like microcontrollers and mobile phones, prioritizing privacy and reducing reliance on cloud infrastructure. Current research emphasizes efficient algorithms and model architectures (e.g., quantized neural networks, vision transformers, and lightweight CNNs) to minimize memory footprint and computational overhead while maintaining accuracy, often incorporating techniques like self-supervised learning, federated learning, and incremental learning. This field is crucial for enabling personalized AI applications at the edge, improving real-time responsiveness, and enhancing data privacy in various domains, including robotics, IoT devices, and mobile applications.