Joint blockchain and federated learning-based offloading in harsh edge computing environments

Abstract

With the popularity of edge computing, numerous Internet of Things (IoT) applications have been developed and applied to various fields. However, for the harsh environment with network fluctuations and potential attacks, traditional task offloading decision-making schemes cannot meet the requirements of real-Time and security. For this reason, we propose a novel task offloading decision framework to cope with the special requirements of the environment. This framework uses a task offloading decision model based on deep reinforcement learning algorithms, and is located on the user side to reduce the impact of network fluctuations. To improve the efficiency and security of the model in harsh edge computing environments, we adopt federated learning and introduce the blockchain into the process of parameter upload and decentralization of federated learning. In addition, we design a new blockchain consensus algorithm to reduce the waste of computing resources and improve the embedding and propagation speeds of the blockchain. Furthermore, we demonstrate the effect of task offloading of this model by performing offloading decisions on a simulation platform.