Federated Learning for Artificial Intelligence in Embedded Systems

Federated Learning (FL) which eliminates the centralized data storage requirement by facilitating model training on diverse edge devices is now a promising paradigm for decentralized machine learning (ML). Applications involving privacy-preserving Artificial Intelligence (AI), including wearable technology, IoT networks, and smart healthcare appliances, can particularly benefit from this solution in embedded systems. By using on-device local data from devices such as sensors, embedded controllers, and smartphones, FL keeps confidential information local, minimizing the data transfer cost and privacy risks. Potentiality, challenges, and key applications of FL integration with embedded systems are addressed in this paper. Device-to-device efficient communication, model updating, and trade-offs between model accuracy and computational resource limitations are some of the issues addressed. Also addressed in the paper are model aggregation, federated optimization methods, and their usage in edge-based AI in real-life applications. Problems with security, system reliability, and heterogeneous data in federated environments are also discussed in the paper. The extensive use of FL in embedded systems is one of the important developments in edge AI solution designing that is more scalable, secure, and privacy-conscious.