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 era of 5G mobile communication, there has been a significant surge inresearch focused on unmanned aerial vehicles (UAVs) and mobile edge computingtechnology. UAVs can serve as intelligent servers in edge computingenvironments, optimizing their flight trajectories to maximize communicationsystem throughput. Deep reinforcement learning (DRL)-based trajectoryoptimization algorithms may suffer from poor training performance due tointricate terrain features and inadequate training data. To overcome thislimitation, some studies have proposed leveraging federated learning (FL) tomitigate the data isolation problem and expedite convergence. Nevertheless, theefficacy of global FL models can be negatively impacted by the highheterogeneity of local data, which could potentially impede the trainingprocess and even compromise the performance of local agents. This work proposesa novel solution to address these challenges, namely personalized federateddeep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.PF-DRL aims to develop individualized models for each agent to address the datascarcity issue and mitigate the negative impact of data heterogeneity.Simulation results demonstrate that the proposed algorithm achieves superiortraining performance with faster convergence rates, and improves servicequality compared to other DRL-based approaches.

Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing