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.