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

• Published in: IEEE transactions on network science and engineering Vol. 8; no. 2; pp. 1055 - 1069
• Main Authors: Wang, Yuntao, Su, Zhou, Zhang, Ning, Benslimane, Abderrahim
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: AI security; Algorithms; Artificial intelligence; blockchain; Cognitive science; Computational modeling; Computer science; Cryptography; Data models; Explicit knowledge; Federated learning; local differential privacy; Machine learning; Model accuracy; Privacy; reinforcement learning; Sensors; Task analysis; Training; UAV; Unmanned aerial vehicles; Utilities
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2020.3014385

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...