Device Multi Modal Model Adaptation

Device multi-modal model adaptation focuses on efficiently fine-tuning large models on resource-constrained devices, enabling personalized AI services without relying solely on cloud infrastructure. Current research emphasizes derivative-free optimization, federated learning, and techniques like parameter-efficient fine-tuning (e.g., LoRA) and sparse subnetworks to reduce memory and computational demands while maintaining accuracy. This area is significant because it allows for privacy-preserving, real-time AI applications on edge devices, impacting fields like personalized recommendations, autonomous driving, and mobile-based AI assistants.