用联邦学习提升5G直播的吞吐量预测，兼顾隐私与性能。
Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications
- 采用联邦学习在异构设备上联合训练，避免数据集中化。
- FedBN配合LSTM/Transformer使平均体验质量提升11.7%。
- 适合需要隐私保护的实时视频流系统部署参考。
准确且自适应的网络吞吐量预测对5G及未来6G中延迟敏感、带宽密集型应用至关重要。然而，现有方法多依赖集中式训练和均匀采集的数据，在非独立同分布（non-IID）的异构移动环境中适用性受限。本文首次系统性地评估了联邦学习（FL）策略在真实5G边缘场景下吞吐量预测的性能。我们对比了三种聚合算法（FedAvg、FedProx、FedBN）与四种时间序列模型（LSTM、CNN、CNN+LSTM、Transformer），使用五个真实世界数据集，系统分析客户端异质性、组大小和历史窗口长度对预测性能的影响。结果表明，FedBN在非IID条件下表现稳定；基于LSTM和Transformer的模型相比CNN基线在R2得分上最高提升80%。尽管Transformer收敛速度是LSTM的一半，但需更长的历史窗口以达到高R2，显示更强上下文依赖性。因此，LSTM在精度、训练轮数与时间跨度间取得良好平衡。为验证端到端可行性，我们将基于联邦学习的预测器集成至实时自适应流媒体管道，结果显示，基于FedBN的LSTM和Transformer模型分别使平均用户体验质量（QoE）提升11.7%和11.4%，同时降低方差。这些发现为构建可扩展、隐私保护且面向边缘的下一代无线网络吞吐量预测系统提供了实用指导。
原文摘要 · Abstract (English)
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.
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