Blockchain-Empowered Decentralized Horizontal Federated Learning for 5G-Enabled UAVs

Chaosheng Feng, Bin Liu, Keping Yu*, Sotirios K. Goudos, Shaohua Wan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.

Original languageEnglish
Pages (from-to)3582-3592
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume18
Issue number5
DOIs
Publication statusPublished - 2022 May 1

Keywords

  • 5G-enabled unmanned aerial vehicles (UAVs)
  • cross-domain authentication
  • federated learning (FL)
  • privacy preservation
  • smart contract

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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