Edge ML

Edge machine learning studies how to run useful prediction and learning directly on devices such as sensors, phones, microcontrollers, and embedded accelerators. Common approaches include quantization, pruning, distillation, and hardware-aware architecture search, alongside federated learning and cloud-edge collaboration when devices need to share updates or offload computation. Applications range from health and agriculture to industrial monitoring, with growing work on continual learning and on-device fine-tuning as conditions change. The central challenge is balancing accuracy, energy, memory, latency, privacy, and robustness, while evaluating systems on their actual hardware and under realistic data and network shifts.