Federated Learning (FL) has emerged as a principled paradigm for privacy-preserving decentralized machine learning, enabling model training across distributed embedded devices without centralizing sensitive data. This review examines FL as applied to resource-constrained embedded and edge AI systems, encompassing its architectural foundations, principal optimization algorithms, application domains, and security mechanisms. We analyze the interplay between FL's theoretical properties and the practical constraints imposed by heterogeneous embedded hardware, non-IID data distributions, bandwidth-limited IoT networks, and adversarial threat models. Application domains examined in depth include s... More >