Federated Learning for Artificial Intelligence in Embedded Systems

Abstract

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 smart healthcare, autonomous vehicles, industrial IoT, precision agriculture, smart home automation, and urban infrastructure, with attention to how FL enables privacy-preserving AI deployment in each context. We further review the principal techniques that make embedded FL viable---including FedAvg, FedProx, model compression, gradient sparsification, differential privacy, secure multi-party computation, and blockchain-secured aggregation---and identify open research gaps in heterogeneous model aggregation and adaptive privacy protection. Future directions encompassing TinyML integration, federated reinforcement learning, and next-generation 5G/6G network infrastructure are discussed. The review aims to provide a technically grounded reference for researchers and practitioners seeking to deploy FL in real-world embedded environments where privacy, energy efficiency, and communication constraints are simultaneously binding.