Federal learning computing unloading computing system and method based on cloud side end

Abstract

The invention discloses a federated learning computing unloading resource allocation system and a method based on a cloud side end, and aims to make an accurate decision for computing task unloading and resource allocation, eliminate the need for solving a combinatorial optimization problem and greatly reduce the computing complexity. Based on cloud side three-layer federated learning, the adjacent advantage of edge nodes to a terminal is comprehensively utilized, core powerful computing resources in cloud computing are also utilized, the problem that the computing resources of the edge nodes are insufficient is solved, a local model is trained at each of multiple clients to predict an unloading task. A global model is formed by periodically executing one-time parameter aggregation at an edge end, the cloud end executes one-time aggregation after the edge executes the periodic aggregation until a global BiLSTM model is formed through convergence, and the global model can intelligently predict the information amount of each unloading task. Therefore, guidance is better provided for calculation unloading and resource allocation.

Description

technical field [0001] The invention relates to computing offloading and resource allocation of a mobile edge computing network driven by a 5G network, and in particular to a cloud-edge-based federated learning computing offloading computing system and method. Background technique [0002] In recent years, driven by the popularity of the Internet of Things, the data generated at the edge of the network has exploded. The inability to guarantee low latency and location awareness cripples the capabilities of traditional cloud computing solutions. According to IDC's forecast, by the end of 2020, more than 50 billion terminals and devices will be connected to the Internet, and more than 50% of the data needs to be analyzed, processed and stored at the edge of the network. However, the traditional "cloud two-body collaborative computing" model can no longer meet the needs of low latency and high bandwidth. Mobile Edge Computing (MEC) is becoming a new and compelling computing pa...

AI Technical Summary

Problems solved by technology

Although edge servers can provide cloud functions to terminals, they may not be able to serve all terminals due to their inherent wireless and limited computing capabilities

On the one hand, the uncertain data volume of offloading tasks and time-varying channel conditions make it difficult to make accurate decisions for computing offloading.

On the other hand, in the distributed heterogeneous edge infrastructure, there is a risk of interception and leakage of user personal sensitive information during the offloading process.

Images