用分数阶机制提升电动车能耗建模的联邦学习稳定性
Fractional Order Federated Learning for Battery Electric Vehicle Energy Consumption Modeling
- 通过动态调整模型拉力来适应本地损失曲面粗糙度
- 引入非整数阶优化，缓解更新方向冲突，提升收敛稳定性
- 轻量模块化设计，适合低参与率、连接不稳的真实场景
联网电池电动车（BEV）上的联邦学习因间歇性连接、客户参与度动态变化及不同运行条件带来的显著客户端差异，面临严重不稳定性。传统FedAvg及许多先进方法在这些现实约束下易出现过度漂移和收敛性能下降。本文提出分数阶粗糙度感知联邦平均（FO-RI-FedAvg），作为FedAvg的轻量级扩展，通过两个互补的客户端机制提升稳定性：(i) 自适应粗糙度感知近端正则化，根据本地损失曲面粗糙度动态调节向全局模型的拉力；(ii) 非整数阶本地优化，引入短期记忆以平滑冲突的更新方向。该方法保持标准FedAvg服务器聚合，仅增加可分摊的逐元素操作，且可独立启用任一组件。在两个真实世界BEV能耗预测数据集VED及其扩展版eVED上的实验表明，相比强基准方法，FO-RI-FedAvg在客户端参与率降低时仍实现更高精度与更稳定收敛。
原文摘要 · Abstract (English)
Federated learning on connected electric vehicles (BEVs) faces severe instability due to intermittent connectivity, time-varying client participation, and pronounced client-to-client variation induced by diverse operating conditions. Conventional FedAvg and many advanced methods can suffer from excessive drift and degraded convergence under these realistic constraints. This work introduces Fractional-Order Roughness-Informed Federated Averaging (FO-RI-FedAvg), a lightweight and modular extension of FedAvg that improves stability through two complementary client-side mechanisms: (i) adaptive roughness-informed proximal regularization, which dynamically tunes the pull toward the global model based on local loss-landscape roughness, and (ii) non-integer-order local optimization, which incorporates short-term memory to smooth conflicting update directions. The approach preserves standard FedAvg server aggregation, adds only element-wise operations with amortizable overhead, and allows independent toggling of each component. Experiments on two real-world BEV energy prediction datasets, VED and its extended version eVED, show that FO-RI-FedAvg achieves improved accuracy and more stable convergence compared to strong federated baselines, particularly under reduced client participation.
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