SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks

Sheikh Salman Hassan; Umer Majeed; Zhu Han; Choong Seon Hong

doi:10.1109/NOMS56928.2023.10154226

SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks

Sheikh Salman Hassan, Umer Majeed, Zhu Han, Choong Seon Hong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.

Original language	English
Title of host publication	Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023
Editors	Kemal Akkaya, Olivier Festor, Carol Fung, Mohammad Ashiqur Rahman, Lisandro Zambenedetti Granville, Carlos Raniery Paula dos Santos
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665477161
DOIs	https://doi.org/10.1109/NOMS56928.2023.10154226
Publication status	Published - 2023
Event	36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023 - Miami, United States Duration: 8 May 2023 → 12 May 2023

Publication series

Name	Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023

Conference

Conference	36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023
Country/Territory	United States
City	Miami
Period	8/05/23 → 12/05/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

6G
federated learning
privacy
security

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/NOMS56928.2023.10154226

Cite this

Hassan, S. S., Majeed, U., Han, Z., & Hong, C. S. (2023). SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks. In K. Akkaya, O. Festor, C. Fung, M. A. Rahman, L. Z. Granville, & C. R. P. dos Santos (Eds.), Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023 (Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NOMS56928.2023.10154226

Hassan, Sheikh Salman ; Majeed, Umer ; Han, Zhu et al. / SFL-LEO : Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks. Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023. editor / Kemal Akkaya ; Olivier Festor ; Carol Fung ; Mohammad Ashiqur Rahman ; Lisandro Zambenedetti Granville ; Carlos Raniery Paula dos Santos. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023).

@inproceedings{084a5e4ee72f4b9f940a3774cc61df3b,

title = "SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks",

abstract = "We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.",

keywords = "6G, federated learning, privacy, security",

author = "Hassan, {Sheikh Salman} and Umer Majeed and Zhu Han and Hong, {Choong Seon}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023 ; Conference date: 08-05-2023 Through 12-05-2023",

year = "2023",

doi = "10.1109/NOMS56928.2023.10154226",

language = "English",

series = "Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023",

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editor = "Kemal Akkaya and Olivier Festor and Carol Fung and Rahman, {Mohammad Ashiqur} and Granville, {Lisandro Zambenedetti} and {dos Santos}, {Carlos Raniery Paula}",

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Hassan, SS, Majeed, U, Han, Z & Hong, CS 2023, SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks. in K Akkaya, O Festor, C Fung, MA Rahman, LZ Granville & CRP dos Santos (eds), Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023. Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023, Institute of Electrical and Electronics Engineers Inc., 36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023, Miami, United States, 8/05/23. https://doi.org/10.1109/NOMS56928.2023.10154226

SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks. / Hassan, Sheikh Salman; Majeed, Umer; Han, Zhu et al.
Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023. ed. / Kemal Akkaya; Olivier Festor; Carol Fung; Mohammad Ashiqur Rahman; Lisandro Zambenedetti Granville; Carlos Raniery Paula dos Santos. Institute of Electrical and Electronics Engineers Inc., 2023. (Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - SFL-LEO

T2 - 36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

AU - Hassan, Sheikh Salman

AU - Majeed, Umer

AU - Han, Zhu

AU - Hong, Choong Seon

PY - 2023

Y1 - 2023

N2 - We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.

AB - We propose using federated learning (FL) in loiv Earth orbit (LEO) satellite networks for the Internet of Remote Things (IoRTs) to enable adaptive learning in massively networked devices while reducing costly traffic in satellite communication (SatCom). In this resource-constrained space setting, FL techniques in LEO satellite-based learning can improve system energy efficiency and save time. However, FL raises security and risk concerns, as local model updates can be used to infer device information by a hostile federated aggregator server in space. To address this, we propose using homomorphic-based encryption and decryption security techniques for federated aggregators and IoRTs. We evaluate the secure learning performance of our proposed framework using simulations on advanced datasets and aggregation approach. The results shoiv that compared to the benchmark scheme, the proposed secured computing networks improve communication overhead and latency performance.

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BT - Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023

A2 - Akkaya, Kemal

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A2 - Fung, Carol

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A2 - dos Santos, Carlos Raniery Paula

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 8 May 2023 through 12 May 2023

ER -

Hassan SS, Majeed U, Han Z, Hong CS. SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks. In Akkaya K, Festor O, Fung C, Rahman MA, Granville LZ, dos Santos CRP, editors, Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023). doi: 10.1109/NOMS56928.2023.10154226

SFL-LEO: Secure Federated Learning Computation Based on LEO Satellites for 6G Non-Terrestrial Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

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