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

In the traditional vehicular network, computing tasks generated by thevehicles are usually uploaded to the cloud for processing. However, since taskoffloading toward the cloud will cause a large delay, vehicular edge computing(VEC) is introduced to avoid such a problem and improve the whole systemperformance, where a roadside unit (RSU) with certain computing capability isused to process the data of vehicles as an edge entity. Owing to the privacyand security issues, vehicles are reluctant to upload local data directly tothe RSU, and thus federated learning (FL) becomes a promising technology forsome machine learning tasks in VEC, where vehicles only need to upload thelocal model hyperparameters instead of transferring their local data to thenearby RSU. Furthermore, as vehicles have different local training time due tovarious sizes of local data and their different computing capabilities,asynchronous federated learning (AFL) is employed to facilitate the RSU toupdate the global model immediately after receiving a local model to reduce theaggregation delay. However, in AFL of VEC, different vehicles may havedifferent impact on the global model updating because of their various localtraining delay, transmission delay and local data sizes. Also, if there are badnodes among the vehicles, it will affect the global aggregation quality at theRSU. To solve the above problem, we shall propose a deep reinforcement learning(DRL) based vehicle selection scheme to improve the accuracy of the globalmodel in AFL of vehicular network. In the scheme, we present the modelincluding the state, action and reward in the DRL based to the specificproblem. Simulation results demonstrate our scheme can effectively remove thebad nodes and improve the aggregation accuracy of the global model.

Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computing