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Wireless distributed learning: a new hybrid split and federated learning approach

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journal contribution
posted on 2022-12-07, 11:31 authored by Xiaolan Liu, Yansha Deng, Toktam Mahmoodi

Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit machine learning (ML) algorithms to provide a wide range of advanced applications, like object detection and video tracking. Conventionally, the ML model training is performed at the BS, known as centralized learning (CL), which causes high communication overhead due to the transmission of large datasets, and potential concerns about UE privacy. To address this, distributed learning algorithms, including federated learning (FL) and split learning (SL), were proposed to train the ML models in a distributed manner via only sharing model parameters. FL requires higher computational resource on the UE side than SL, while SL has larger communication overhead when the local dataset is large. To effectively train an ML model considering the diversity of UEs with different computational capabilities and channel conditions, we first propose a novel distributed learning architecture, a hybrid split and federated learning (HSFL) algorithm by reaping the parallel model training mechanism of FL and the model splitting structure of SL. We then provide its convergence analysis under non-independent and identically distributed (non-IID) data with random UE selection scheme. By conducting experiments on training two ML models, Net and AlexNet, in wireless UAV networks, our results demonstrate that the HSFL algorithm achieves higher learning accuracy than FL and less communication overhead than SL under IID and non-IID data, and the learning accuracy of HSFL algorithm increases with the increasing number of the split training UEs. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better wireless channel quality and larger local model updates for model training in each round. Numerical results demonstrate it achieves higher learning accuracy than BC, MAB-BC and MAB-BN2 UE selection scheme under non-IID, Dirichlet-nonIID and Dirichlet-Imbalanced data.

Funding

European Union’s Horizon 2020 Research and Innovation Program

Virtual Presence in Moving Objects through 5G

European Commission

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Smart Solutions Towards Cellular-Connected Unmanned Aerial Vehicles System (AUTONOMY)

Engineering and Physical Sciences Research Council

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Innovate UK through the Project “Automation of Network edge Infrastructure and Applications with aRtificiAl intelligence (ANIARA)” under Grant 127811561

History

School

  • Loughborough University London

Published in

IEEE Transactions on Wireless Communications

Volume

22

Issue

4

Pages

2650 - 2665

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2022-09-26

Publication date

2022-10-18

Copyright date

2022

ISSN

1536-1276

eISSN

1558-2248

Language

  • en

Depositor

Dr Xiaolan Liu. Deposit date: 1 December 2022