发表机构
Koc University（科克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对VANET的异构任务、非IID数据等挑战，本文提出AERO-HMTFL框架，通过三权重聚类与可靠性感知聚合等设计，提升了准确率、稳定性并降低了通信开销。
AI 中文摘要
车载自组网（Vehicular Ad hoc Networks, VANET）越来越依赖联邦学习（Federated Learning, FL）实现无需共享原始感知数据的协同智能。然而，现有大多数车载联邦学习框架假设所有车辆针对单一公共任务训练全局模型，这限制了其在实际车载环境中的适用性——实际环境中车辆可能执行异构学习任务，且数据非独立同分布（non-IID）、连接间歇性强、车辆移动性高。为应对这些挑战，本文针对动态多跳分簇VANET提出了基于自动编码器的可靠性优化分层多任务联邦学习（AutoEncoder-based Reliability-Optimized Hierarchical Multi-Task Federated Learning, AERO-HMTFL）框架。该框架引入三权重聚类度量，同时考虑车辆移动性、共享模型相似度和任务亲和性，以生成移动稳定、语义对齐的簇。每辆车采用拆分模型架构，包含共享的基于自动编码器的表示模块和多个任务特定头，仅交换共享自动编码器参数，任务头保持本地。为提升鲁棒性，簇头基于历史验证性能和参与频率执行感知可靠性的聚合，演进分组核心网（Evolved Packet Core, EPC）则跨簇执行共享自动编码器的全局融合。大量仿真表明，与多任务联邦学习基准相比，AERO-HMTFL在EPC级的持续准确率最高提升13%，学习动态更稳定，EPC级数据包传输量减少约87%-97%；在短距离连接下，其收敛所需通信轮次也减少约13%-29%。
英文摘要
Vehicular Ad hoc Networks (VANETs) increasingly rely on federated learning (FL) to enable collaborative intelligence without sharing raw sensory data. However, most existing vehicular FL frameworks assume that all vehicles train a single global model for a common task, which limits their applicability in practical vehicular environments where vehicles may perform heterogeneous learning tasks under non-independent and identically distributed (non-IID) data, intermittent connectivity, and high mobility. To address these challenges, this paper proposes an AutoEncoder-based Reliability-Optimized Hierarchical Multi-Task Federated Learning (AERO-HMTFL) framework for dynamic multi-hop clustered VANETs. The proposed framework introduces a tri-weighted clustering metric that jointly considers vehicular mobility, shared-model similarity, and task affinity to produce mobility-stable, semantically aligned clusters. Each vehicle employs a split-model architecture comprising a shared autoencoder-based representation module and multiple task-specific heads, with only the shared autoencoder parameters exchanged while the task heads remain local. To improve robustness, cluster heads perform reliability-aware aggregation based on historical validation performance and participation frequency, while the Evolved Packet Core (EPC) conducts global shared-autoencoder fusion across clusters. Extensive simulations demonstrate that, compared with the multi-task federated learning benchmarks, AERO-HMTFL achieves up to 13% higher sustained EPC-level accuracy, exhibits more stable learning dynamics, and reduces EPC-level packet transmissions by approximately 87-97%. Under short-range connectivity, it also requires approximately 13-29% fewer communication rounds to converge.
