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

计算机科学 拥挤感测 联合学习 差别隐私 强化学习 数据共享 信息隐私 人工智能 脆弱性(计算) 机器学习 分布式计算 计算机安全 数据挖掘 医学 病理 替代医学
作者
Yuntao Wang,Zhou Su,Ning Zhang,Abderrahim Benslimane
出处
期刊:IEEE Transactions on Network Science and Engineering [Institute of Electrical and Electronics Engineers]
卷期号:8 (2): 1055-1069 被引量:203
标识
DOI:10.1109/tnse.2020.3014385
摘要

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.
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