Learning in the Air: Secure Federated Learning for UAV-Assisted Crowdsensing

【Author】 Wang, Yuntao; Su, Zhou; Zhang, Ning; Benslimane, Abderrahim

【Source】IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING

【Abstract】Unmanned aerial vehicles (UAVs) combined with artificial intelligence (AI) have opened a revolutionized way for mobile crowdsensing (MCS). Conventional AI models, built on aggregation of UAVs' sensing data (typically contain private and sensitive user information), may arise severe privacy and data misuse concerns. Federated learning, as a promising distributed AI paradigm, has opened up possibilities for UAVs to collaboratively train a shared global model without revealing their local sensing data. However, there still exist potential security and privacy threats for UAV-assisted crowdsensing with federated learning due to vulnerability of central curator, unreliable contribution recording, and low-quality shared local models. In this paper, we propose SFAC, a secure federated learning framework for UAV-assisted MCS. Specifically, we first introduce a blockchain-based collaborative learning architecture for UAVs to securely exchange local model updates and verify contributions without the central curator. Then, by applying local differential privacy, we design a privacy-preserving algorithm to protect UAVs' privacy of updated local models with desirable learning accuracy. Furthermore, a two-tier reinforcement learning-based incentive mechanism is exploited to promote UAVs' high-quality model sharing when explicit knowledge of network parameters are not available in practice. Extensive simulations are conducted, and the results demonstrate that the proposed SFAC can effectively improve utilities for UAVs, promote high-quality model sharing, and ensure privacy protection in federated learning, compared with existing schemes.

【Keywords】AI security; blockchain; federated learning; local differential privacy; reinforcement learning; UAV

【标题】空中学习：无人机辅助群智感知的安全联邦学习

【摘要】无人机 (UAV) 与人工智能 (AI) 相结合，为移动群智感知 (MCS) 开辟了一条革命性的道路。建立在无人机传感数据（通常包含私人和敏感用户信息）聚合之上的传统 AI 模型可能会引起严重的隐私和数据滥用问题。联邦学习作为一种很有前途的分布式 AI 范式，为无人机在不泄露其本地传感数据的情况下协作训练共享的全球模型开辟了可能性。然而，由于中央策展人的脆弱性、不可靠的贡献记录和低质量的共享本地模型，联邦学习的无人机辅助众感仍然存在潜在的安全和隐私威胁。在本文中，我们提出了 SFAC，一种用于无人机辅助 MCS 的安全联邦学习框架。具体来说，我们首先为无人机引入了一种基于区块链的协作学习架构，以在没有中央管理者的情况下安全地交换本地模型更新并验证贡献。然后，通过应用局部差分隐私，我们设计了一种隐私保护算法，以保护无人机更新局部模型的隐私，并具有理想的学习精度。此外，当网络参数的明确知识在实践中不可用时，利用基于两层强化学习的激励机制来促进无人机的高质量模型共享。进行了广泛的模拟，结果表明，与现有方案相比，所提出的 SFAC 可以有效地提高无人机的效用，促进高质量的模型共享，并确保联邦学习中的隐私保护。

【关键词】人工智能安全；区块链；联邦学习；局部差分隐私；强化学习；无人机

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】5.033

【翻译者】石东瑛