H-FLTN通过分层联邦学习预测电动车充电时空需求，兼顾隐私与效率。
H-FLTN: A Privacy-Preserving Hierarchical Framework for Electric Vehicle Spatio-Temporal Charge Prediction
- 分三层架构：车端、社区能源系统、数据中心，协同预测充电行为。
- 动态限流与轮换机制使训练时间随车辆增多保持恒定，提升效率。
- 结合秘密共享与点对点加密，仅交换模型密钥，保护用户隐私。
电动汽车的普及给能源供应商带来挑战，包括充电时间预测、用户隐私保护及移动网络中的资源高效管理。本文提出分层联邦学习变换器网络（H-FLTN）框架，采用三层次架构：电动车、社区分布式能源管理系统（DERMS）和能源提供商数据中心，实现对电动车充电需求的精准时空预测，并保障隐私。基于变换器的时间序列建模捕捉复杂充电行为依赖关系。隐私保护通过安全聚合、加法秘密共享及带增强的点对点（P2P）共享实现，仅交换模型权重密钥，确保通信安全。为提升训练效率与资源管理，引入动态客户端限流机制（DCCM）与客户端轮换管理（CRM），在参与电动车数量增加时，使训练时间复杂度从线性降至常数。基于大规模真实车辆移动数据的仿真结果表明，该框架可显著提升能效预测、资源分配与电网稳定性，助力未来智慧城市的可持续发展。
原文摘要 · Abstract (English)
The widespread adoption of Electric Vehicles (EVs) poses critical challenges for energy providers, particularly in predicting charging time (temporal prediction), ensuring user privacy, and managing resources efficiently in mobility-driven networks. This paper introduces the Hierarchical Federated Learning Transformer Network (H-FLTN) framework to address these challenges. H-FLTN employs a three-tier hierarchical architecture comprising EVs, community Distributed Energy Resource Management Systems (DERMS), and the Energy Provider Data Centre (EPDC) to enable accurate spatio-temporal predictions of EV charging needs while preserving privacy. Temporal prediction is enhanced using Transformer-based learning, capturing complex dependencies in charging behavior. Privacy is ensured through Secure Aggregation, Additive Secret Sharing, and Peer-to-Peer (P2P) Sharing with Augmentation, which allow only secret shares of model weights to be exchanged while securing all transmissions. To improve training efficiency and resource management, H-FLTN integrates Dynamic Client Capping Mechanism (DCCM) and Client Rotation Management (CRM), ensuring that training remains both computationally and temporally efficient as the number of participating EVs increases. DCCM optimises client participation by limiting excessive computational loads, while CRM balances training contributions across epochs, preventing imbalanced participation. Our simulation results based on large-scale empirical vehicle mobility data reveal that DCCM and CRM reduce the training time complexity with increasing EVs from linear to constant. Its integration into real-world smart city infrastructure enhances energy demand forecasting, resource allocation, and grid stability, ensuring reliability and sustainability in future mobility ecosystems.
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