Hyperdimensional Computing-Based Federated Learning in Mobile Robots Through Synthetic Oversampling

Abstract

Traditional federated learning frameworks, often reliant on deep neural networks, face challenges related to computational demands and privacy risks. In this paper, we present a novel Hyperdimensional (HD) Computing-based federated learning framework designed for resource-constrained mobile robots. Unlike other HD-based learning, our approach introduces dynamic encoding, which improves both model accuracy and privacy by continuously updating hypervector representations. To further address the issue of imbalanced data, especially prevalent in robotics tasks, we propose a hypervector oversampling technique, enhancing model robustness. Extensive evaluations on LiDAR-equipped mobile robots demonstrate that our oversampling method outperforms state-of-the-art HD computing frameworks, achieving up to a 22.9% increase in accuracy while maintaining computational efficiency.