CROWDFL: Privacy-preserving mobile crowdsensing system via federated learning

• Main Authors: ZHAO, Bowen, LIU, Ximeng, CHEN, Wei-Neng, DENG, Robert H.
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2023
• 主題: Crowdsensing; federated learning; homomorphic encryption; incentive; privacy protection; Information Security; Numerical Analysis and Scientific Computing
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/8186

As an emerging sensing data collection paradigm, mobile crowdsensing (MCS) enjoys good scalability and low deployment cost but raises privacy concerns. In this paper, we propose a privacy-preserving MCS system called CROWDFL by seamlessly integrating federated learning (FL) into MCS. At a high level...