面向VANET的编码器共享分层联邦多任务学习

中文摘要 AI 辅助

大多数面向车载自组织网络（vehicular ad hoc networks, VANETs）的联邦学习框架假设所有车辆协同训练一个单一模型以完成共同任务。这一假设限制了它们在实际车载环境中的适用性，因为在真实环境中，车辆可能执行异构但相关的感知任务，且这些任务具有不同的输出空间。本文提出了编码器共享分层多任务联邦学习（encoder-sharing hierarchical multi-task federated learning, EN-HMTFL），该方法将基于聚类的分层联邦学习与全局共享编码器及车辆本地解码器相结合。EN-HMTFL使得执行不同任务的车辆能够协同学习一个可迁移的特征表示，同时将各自任务特定的模型保留在本地。在分层结构中，仅编码器被交换和聚合，而原始数据和本地解码器参数则保留在车辆端。所提出的框架在MNIST和GTSRB数据集上，于不同的车载场景中进行了评估。在所有评估的场景中，与所比较的表示共享基准相比，EN-HMTFL的准确率提升了最高达24.0%。在EN-HMTFL较早收敛的场景中，通信轮次减少最多可达69轮（28.8%）。

英文摘要

Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task. This assumption limits their applicability to practical vehicular environments, where vehicles may perform heterogeneous but related perception tasks with different output spaces. This paper proposes encoder-sharing hierarchical multi-task federated learning (EN-HMTFL), which integrates cluster-based hierarchical federated learning with a globally shared encoder and vehicle-local decoders. EN-HMTFL enables vehicles performing different tasks to collaboratively learn a transferable feature representation while preserving their task-specific models locally. Only the encoder is exchanged and aggregated through the hierarchy, whereas raw data and local decoder parameters remain at the vehicles. The proposed framework is evaluated on the MNIST and GTSRB datasets in different vehicular scenarios. Across the evaluated scenarios, EN-HMTFL improves accuracy by up to 24.0% relative to the compared representation-sharing benchmark. In scenarios where EN-HMTFL converges earlier, the reduction reaches up to 69 communication rounds (28.8%).

发表机构

• Koç University（科奇大学）

机构由 AI 辅助整理，请以论文原文为准。