【Author】
Feng, Chaosheng; Liu, Bin; Yu, Keping; Goudos, Sotirios K.; Wan, Shaohua
【Source】IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
【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
【标题】区块链赋能 5G 无人机的去中心化水平联邦学习
【摘要】在工业 4.0 的推动下，支持 5G 的无人机（UAV；也称为无人机）广泛应用于各个行业。然而，5G 网络的开放性威胁着数据的安全共享。特别是隐私泄露会给用户带来严重的损失。作为一种新的机器学习范式，联邦学习 (FL) 通过允许共享数据模型而不是原始数据来避免隐私泄露。不幸的是，传统的 FL 框架强烈依赖于集中式聚合服务器，如果服务器受到攻击，将导致系统崩溃。未经授权的参与者可能会发起投毒攻击，从而降低模型的可用性。此外，通信障碍阻碍了大量跨域学习设备之间的协作。为了解决上述问题，提出了一种基于区块链的去中心化水平 FL 框架。跨域无人机的认证是通过多重签名智能合约完成的。全局模型更新是通过使用这些智能合约而不是集中式服务器来计算的。大量的实验结果表明，该方案实现了跨域认证的高效率和良好的准确性。
【关键词】区块链；验证;工业物联网；训练;数据模型；无人机；信息学；支持 5G 的无人机（UAV）；跨域认证；联邦学习（FL）；隐私保护；智能合约
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