The growing prevalence of encrypted network traffic has rendered traditional payload-based inspection ineffective, shifting attention toward flow-level statistical analysis combined with machine learning. At the same time, privacy regulations and distributed network architectures make centralised data collection increasingly impractical, motivating federated learning as a privacy-preserving alternative. Despite its promise, deploying federated learning for encrypted traffic classification in realistic environments remains challenging, particularly under heterogeneous client data distributions that arise when different network sites observe different subsets of services. This paper examines how server-side aggregation affects federated QUIC traffic classification under such heterogeneous conditions. We use a five-class Google QUIC dataset and represent each flow with eight statistical features derived from packet size and timing. We compare a centralised baseline with federated learning under three client partitions: mixed-label clients (C1), service-based single-class clients (C2), and hash-based semi-IID clients (C3). For each case, we evaluate four Flower aggregation strategies: FedAvg, FedAdam, FedAvgM, and FedYogi. Results show that client distribution has a greater impact on performance than the choice of aggregation strategy. Federated models match or closely approach centralised performance in C1 and C3, with accuracy up to 0.9969 and macro-AUC near 1.0. In C2, accuracy drops due to extreme label skew, but adaptive aggregation mitigates the effect. FedYogi achieves the best C2 accuracy of 0.9287, while FedAvgM attains the highest C2 macro-AUC of 0.9885. ROC curves and confusion matrices confirm that the choice of aggregation matters mainly under severe heterogeneity.