King's College London

Research portal

Energy Efficient User Scheduling for Hybrid Split and Federated Learning in Wireless UAV Networks

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationIEEE ICC 2022
PublisherIEEE Communications Society
Accepted/In press2022


  • m74574-liu paper

    m74574_liu_paper.pdf, 403 KB, application/pdf

    Uploaded date:28 Jan 2022

    Version:Accepted author manuscript

King's Authors


The use of unmanned aerial vehicles (UAVs) as flying users provides various applications by exploiting machine learning (ML) algorithms. Recently, distributed learning algorithms, federated learning (FL) and split learning (SL), have been exploited to train ML models distributedly via sharing model parameters rather than large raw datasets in the conventional centralized learning algorithms. Considering the diversity of users with heterogeneous resources, computation capabilities, and data distributions, we propose a hybrid split and federated learning (HSFL) framework that allows users to select either split training (ST) or federated training (FT) method based on the characteristics of the users in wireless UAV networks. Due to unreliable wireless channels and the limited energy of the users, we further formulate a user scheduling and training method selection problem within HSFL framework as a Multiple-Choice Knapsack Problem (MCKP) and propose an energy-efficient user scheduling algorithm to select a subset of users in each round and schedule each user with either ST or FT method. The simulations demonstrate that our proposed HSFL framework consumes less energy while having the same good test accuracy performance compared to the currently distributed learning algorithms, and the proposed user scheduling algorithm achieves energy-efficient selection of ST or FT method under different distributions.

Download statistics

No data available

View graph of relations

© 2020 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454