Keep Your Data Locally: Federated-Learning-Based Data Privacy Preservation in Edge Computing

Recently, edge computing has attracted significant interest due to its ability to extend cloud computing utilities and services to the network edge with low response times and communication costs. In general, edge computing requires mobile users to upload their raw data to a centralized data server for further processing. However, these data usually contain sensitive information about mobile users that the users do not want to reveal, such as sexual orientation, political stance, health status, and service access history. The transmission of user data increases the leakage risk of data privacy since many extra devices can get access to these data. In this article, we attempt to keep the data of edge devices and end users on their local storage to resist the leakage of user privacy. To this end, we integrate federated learning and edge computing to propose P2FEC, a privacy-preserving framework that can construct a unified deep learning model across multiple users or devices without uploading their data to a centralized server. Furthermore, we use membership inference attacks as a case study for the privacy analysis of edge computing. The experiments show that the model constructed by our framework can achieve similar prediction performance and stricter protection of data privacy, compared to the model trained by standard edge computing.

Automated summary

Edge computing is gaining popularity for extending cloud computing services to the network edge with lower response times and communication costs. The integration of federated learning and edge computing in the P2FEC framework allows for constructing a unified deep learning model without uploading data to a centralized server, providing stricter protection of data privacy. Membership inference attacks were used as a case study to show that the model built by this framework achieves similar prediction performance and enhances data privacy protection compared to standard edge computing.