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

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


研究成果: Article査読

104 被引用数 (Scopus)


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.

ジャーナルIEEE Transactions on Industrial Informatics
出版ステータスPublished - 2022 5月 1

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 情報システム
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学


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