Federated learning based caching in fog computing for future smart cities

Abstract

Edge devices in the Internet of Things (IoT) networks are responsible for the continuous generation and communication of massive chunks of transient data to the fog devices for data processing. However, resources at the fog nodes are limited. Therefore, objective of the work is efficient caching, which is achieved using Federated Learning (FL), where the data used for learning is not gathered centrally but remains where it is produced. Therefore, the heavy transmission of the data to the fog nodes for learning is not required. Simulation results depict the advantages of using this approach over other centralized approaches.