Accurate and adaptive network throughput prediction is essential for latency-sensitive and bandwidth-intensive applications in 5G and emerging 6G networks. However, most existing methods rely on centralized training with uniformly collected data, limiting their applicability in heterogeneous mobile environments with non-IID data distributions. This paper presents the first comprehensive benchmarking of federated learning (FL) strategies for throughput prediction in realistic 5G edge scenarios. We evaluate three aggregation algorithms - FedAvg, FedProx, and FedBN - across four time-series architectures: LSTM, CNN, CNN+LSTM, and Transformer, using five diverse real-world datasets. We systematically analyze the effects of client heterogeneity, cohort size, and history window length on prediction performance. Our results reveal key trade-offs among model complexities, convergence rates, and generalization. It is found that FedBN consistently delivers robust performance under non-IID conditions. On the other hand, LSTM and Transformer models outperform CNN-based baselines by up to 80% in R2 scores. Moreover, although Transformers converge in half the rounds of LSTM, they require longer history windows to achieve a high R2, indicating higher context dependence. LSTM is, therefore, found to achieve a favorable balance between accuracy, rounds, and temporal footprint. To validate the end-to-end applicability of the framework, we have integrated our FL-based predictors into a live adaptive streaming pipeline. It is seen that FedBN-based LSTM and Transformer models improve mean QoE scores by 11.7% and 11.4%, respectively, over FedAvg, while also reducing the variance. These findings offer actionable insights for building scalable, privacy-preserving, and edge-aware throughput prediction systems in next-generation wireless networks.