One for All: Traffic Prediction at Heterogeneous 5G Edge with Data-Efficient Transfer Learning

By placing the computing, storage and networking resources close to the end users, distributed edge computing greatly benefits the performance of 5G communication systems. However, as a tradeoff, resources on the edge are usually limited and imbalanced among the heterogeneous edge nodes. To overcome this drawback, this paper proposes a Transfer Learning based Prediction (TLP) framework that allows the edge nodes to share their resources and data in an efficient manner. In particular, the TLP framework focuses on the prediction of the future traffic load, which is a key reference for many automated network functions. To enhance the efficiency of data and bandwidth, TLP first learns a base model on a data-abundant edge node (the source), and then transfers this model (instead of data) to other data-limited nodes (the targets). To achieve a delicate balance between maintaining common features and learning target-specific features, we develop a new transfer learning technique named Similarity-based Elastic Weight Consolidation (SEWC), and integrate it into TLP. Experiments on real-world data illustrate that, compared to the state-of-the-art methods, TLP-SEWC reduces the Mean Absolute Error (MAE) of traffic prediction by up to 57.9%.