Deep Federated Learning for Autonomous Driving. (arXiv:2110.05754v2 [cs.LG] UPDATED)

Autonomous driving is an active research topic in both academia and industry.
However, most of the existing solutions focus on improving the accuracy by
training learnable models with centralized large-scale data. Therefore, these
methods do not take into account the user's privacy. In this paper, we present
a new approach to learn autonomous driving policy while respecting privacy
concerns. We propose a peer-to-peer Deep Federated Learning (DFL) approach to
train deep architectures in a fully decentralized manner and remove the …

arxiv autonomous autonomous driving driving federated learning learning