RESUMO
Introduction: Res-FLNet presents a cutting-edge solution for addressing autonomous driving tasks in the context of multimodal sensing robots while ensuring privacy protection through Federated Learning (FL). The rapid advancement of autonomous vehicles and robotics has escalated the need for efficient and safe navigation algorithms that also support Human-Robot Interaction and Collaboration. However, the integration of data from diverse sensors like cameras, LiDARs, and radars raises concerns about privacy and data security. Methods: In this paper, we introduce Res-FLNet, which harnesses the power of ResNet-50 and LSTM models to achieve robust and privacy-preserving autonomous driving. The ResNet-50 model effectively extracts features from visual input, while LSTM captures sequential dependencies in the multimodal data, enabling more sophisticated learning control algorithms. To tackle privacy issues, we employ Federated Learning, enabling model training to be conducted locally on individual robots without sharing raw data. By aggregating model updates from different robots, the central server learns from collective knowledge while preserving data privacy. Res-FLNet can also facilitate Human-Robot Interaction and Collaboration as it allows robots to share knowledge while preserving privacy. Results and discussion: Our experiments demonstrate the efficacy and privacy preservation of Res-FLNet across four widely-used autonomous driving datasets: KITTI, Waymo Open Dataset, ApolloScape, and BDD100K. Res-FLNet outperforms state-of-the-art methods in terms of accuracy, robustness, and privacy preservation. Moreover, it exhibits promising adaptability and generalization across various autonomous driving scenarios, showcasing its potential for multi-modal sensing robots in complex and dynamic environments.