Approach | Area | Main Idea |
Andrés et al. [72] | Geography | Investigate the application of differential privacy techniques in protecting customer geolocation data. |
Zhao et al. [74] | Internet of Vehicles | Propose a novel local differential privacy mechanism, named as Three-Outputs, to protect the privacy of client’s data, and propose an LDP-FedSGD to train the model. |
Cao et al. [75] | Power Internet of Things | Propose IFed, a novel federated learning framework that takes into account the trade-off between local differential privacy, data utility, and resource consumption, to allow electric providers who normally have adequate computing resources to assist users in the Power Internet of Things. |
Jia et al. [76] | Industrial Internet of Things | Propose a blockchain-enabled differential private federated learning in Industrial Internet of Things (IIoT). |
Olowononi et al. [77] | Vehicular Cyber-physical Systems | Propose a differential-private federated learning framework to improve the resiliency of vehicular cyber-physical systems to adversarial attacks in connected vehicles. |
Liu et al. [78] | Medical Institutions | Propose a federated learning framework for distributed medical institutions to collaboratively learn a prediction model. |
Kaissis et al. [79] | Medical Image Analysis | Propose a differential private federated learning framework for image analysis, named PriMIA, and theoretically and empirically evaluate its performance and privacy guarantees. |
Liu et al. [82] | Wireless Sensor Networks | Propose a secure and reliable federated learning algorithm for wireless sensor networks. |