用联邦梯度提升树实现隐私保护的共享单车需求预测。
Bikelution: Federated Gradient-Boosting for Scalable Shared Micro-Mobility Demand Forecasting
- 基于联邦学习与梯度提升树，实现分布式隐私保护建模。
- 在三个真实数据集上，预测精度接近中心化模型，优于现有方法。
- 适合关注隐私安全与中长期出行预测的交通系统研究者。
无桩共享单车系统的快速发展产生了海量时空数据，可用于车队调度、缓解拥堵和促进可持续出行。然而，自行车需求受多种外部因素影响，传统时间序列模型难以应对。集中式机器学习虽能实现高精度预测，但在边缘设备分散数据时面临隐私和带宽问题。为此，我们提出Bikelution，一种基于梯度提升树的高效联邦学习方案，在保障隐私的同时，可实现长达六小时的中短期需求预测。在三个真实世界共享单车数据集上的实验表明，Bikelution性能与基于中心化学习的变体相当，并优于当前最先进方法。结果验证了隐私感知需求预测的可行性，揭示了联邦学习与集中式学习之间的权衡关系。
原文摘要 · Abstract (English)
The rapid growth of dockless bike-sharing systems has generated massive spatio-temporal datasets useful for fleet allocation, congestion reduction, and sustainable mobility. Bike demand, however, depends on several external factors, making traditional time-series models insufficient. Centralized Machine Learning (CML) yields high-accuracy forecasts but raises privacy and bandwidth issues when data are distributed across edge devices. To overcome these limitations, we propose Bikelution, an efficient Federated Learning (FL) solution based on gradient-boosted trees that preserves privacy while delivering accurate mid-term demand forecasts up to six hours ahead. Experiments on three real-world BSS datasets show that Bikelution is comparable to its CML-based variant and outperforms the current state-of-the-art. The results highlight the feasibility of privacy-aware demand forecasting and outline the trade-offs between FL and CML approaches.
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