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

TY - CONF

T1 - Energy Efficient User Scheduling for Hybrid Split and Federated Learning in Wireless UAV Networks

AU - Liu, Xiaolan

AU - Deng, Yansha

AU - Mahmoodi, Toktam

N1 - Funding Information: ACKNOWLEDGEMENTS This work has received funding from Innovate UK in the project ANIARA: Automation of Network edge Infrastructure and Applications with aRtificiAl intelligence, CELTIC-NEXT under grant agreement. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85137674088&partnerID=8YFLogxK

U2 - 10.1109/ICC45855.2022.9882277

DO - 10.1109/ICC45855.2022.9882277

M3 - Paper

AN - SCOPUS:85137674088

T2 - 2022 IEEE International Conference on Communications, ICC 2022

Y2 - 16 May 2022 through 20 May 2022

ER -