English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Federated learning has been widely applied in autonomous driving since itenables training a learning model among vehicles without sharing users' data.However, data from autonomous vehicles usually suffer from thenon-independent-and-identically-distributed (non-IID) problem, which may causenegative effects on the convergence of the learning process. In this paper, wepropose a new contrastive divergence loss to address the non-IID problem inautonomous driving by reducing the impact of divergence factors fromtransmitted models during the local learning process of each silo. We alsoanalyze the effects of contrastive divergence in various autonomous drivingscenarios, under multiple network infrastructures, and with differentcentralized/distributed learning schemes. Our intensive experiments on threedatasets demonstrate that our proposed contrastive divergence losssignificantly improves the performance over current state-of-the-artapproaches.

Reducing Non-IID Effects in Federated Autonomous Driving with Contrastive Divergence Loss