EdgeAI aims at the widespread deployment of AI on edge devices. To this end, a critical requirement of future ML systems is to enable on-device automated training and inference in distributed settings, wherever and whenever data, devices, or users are present, without sending the training (possibly sensitive) data to the cloud or incurring long response times. Starting from these overarching considerations, we consider on-device distributed learning, the hardware it runs on, and their co-design to allow for efficient federated learning and resource-aware deployment on edge devices. We hope to convey the excitement of working in this problem space that brings together topics in ML, optimization, communications, and application-hardware (co-)design.