On-Device Training

On-device training studies how models can learn from local data without relying on cloud retraining, supporting personalization, continual adaptation, and privacy-sensitive applications. Approaches range from full fine-tuning and parameter-efficient methods such as LoRA to prototype updates, local learning rules, and zeroth-order optimization that reduce memory or avoid backpropagation. Hardware and runtime work uses quantization, pruning, specialized accelerators, and memory-aware scheduling to fit training on phones, microcontrollers, FPGAs, and neuromorphic devices; evaluations span tasks such as keyword spotting, biosignal classification, and image recognition. A central tension is preserving accuracy while meeting tight memory, energy, and thermal limits, and many methods still need validation under sustained use and real-world distribution shifts.