This paper introduces CFL-2S, a novel two-stage clustered federated learning method designed for reliable traffic prediction in managed Wi-Fi networks. It addresses the challenge of data heterogeneity among Access Points by intelligently forming clusters, achieving superior predictive performance while minimizing communication and energy overhead among clustered approaches.