Federated learning (FL) has emerged as a promising approach to enable decentralized machine learning directly at the edge, in order to enhance users’ privacy, comply with regulations, and reduce development costs. In this talk, I will provide an overview of FL and highlight three fundamental challenges for landing FL into practice: (1) privacy and security guarantees for FL; (2) label scarcity at the edge; and (3) FL over resource-constrained edge nodes. I will also provide a brief overview of FedML (https://fedml.ai), which is a platform that enables zero-code, lightweight, cross-platform, and provably secure federated learning and analytics.