Efficient Wireless Traffic Prediction at the Edge: A Federated Meta-Learning Approach

Abstract

Wireless traffic prediction plays a vital role in managing high dynamic and low latency communication networks, especially in 6G wireless networks. Regarding data and computing resources constraints in edge devices, federated wireless traffic prediction has attracted considerable interest. However, federated learning is limited to dealing with heterogeneous scenarios and unbalanced data availability. Along this line, we propose an efficient federated meta-learning approach to learn a sensitive global model with knowledge collected from different regions. The global model can efficiently adapt to the heterogeneous local scenarios by processing only one or a few steps of fine-tuning on the local data sets. Additionally, distance-based weighted model aggregation is designed to capture the dependencies among different regions for better spatial-temporal prediction. We evaluate the performance of the proposed scheme by comparing it with the conventional federated learning approaches and other commonly used benchmarks for traffic prediction. The extensive simulation results reveal that the proposed scheme outperforms the benchmarks.