A Federated Artificial Intelligence Framework for Privacy-Preserving Traffic Prediction in Next-Generation Wireless Networks

Abstract

Accurate wireless traffic prediction is a prerequisite for proactive radio resource scheduling, energy management, and service assurance in beyond 5G and sixth generation networks. Centralized machine learning can exploit large traffic histories but raises data governance, backhaul and privacy concerns. This paper develops a federated artificial intelligence framework in which radio access nodes train a shared temporal forecasting model while retaining raw measurements locally. The architecture integrates robust preprocessing, a temporal convolution and gated recurrent predictor, weighted model aggregation, client participation scheduling, update clipping, optional differential privacy and secure aggregation. A reproducible evaluation protocol specifies temporal holdout partitions, heterogeneous client distributions, baselines, ablations, uncertainty analysis and privacy auditing. To illustrate reporting and interpretation, numerical tables and plots are provided as synthetic worked examples, not as measurements from a completed experiment. The proposed design clarifies the engineering tradeoffs between forecast error, communications cost, privacy expenditure and fairness across cells. Its central contribution is a testable systems blueprint rather than an unsupported empirical performance claim.