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

【Author】 Feng, Chaosheng; Liu, Bin; Yu, Keping; Goudos, Sotirios K.; Wan, Shaohua

【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

【影响因子】11.648

【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.

【Keywords】Blockchains; Authentication; Industrial Internet of Things; Training; Data models; Drones; Informatics; 5G-enabled unmanned aerial vehicles (UAVs); cross-domain authentication; federated learning (FL); privacy preservation; smart contract

【发表时间】2022 MAY

【收录时间】2022-02-18

【文献类型】期刊

【主题类别】

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