Drones, Volume 8, Issue 7 (July 2024) – 67 articles
Cover Story (view full-size image):
With edge computing, IoT devices like UAVs can process data efficiently by reducing the need for onboard processing power and the data transfer time for real-time applications. Meanwhile, Federated Learning (FL) allows UAVs to improve model training by learning from others without direct data access to preserve data privacy. However, the Non-IID data issue in UAV logistics has a significant impact on the quality of AI models. To address such an issue, we propose a novel cloud–edge–end collaborative FL framework that employs clustering and cosine similarity to integrate suitable local models into the global model to effectively address the Non-IID data issue. The proposed framework has been successfully implemented in our EXPRESS2.0 UAV delivery system. View this paper
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