Federated Learning in Autonomous Vehicles Using Cross-Border Training
Summary Federated learning is revolutionizing the development of autonomous vehicles (AVs), particularly in cross-country scenarios where diverse data sources and conditions are crucial. Unlike traditional machine learning methods that require centralized data storage, federated learning enables AVs to collaboratively train algorithms using locally collected data while keeping the data decentralized. This approach enhances privacy and security, as sensitive data never leaves the country, and improves the robustness of the models by incorporating a wide range of driving environments and situations....