RoADTrain: Route-Assisted Decentralized Peer Model Training Among Connected Vehicles
RoADTrain: Route-Assisted Decentralized Peer Model Training Among Connected Vehicles
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DOI：
10.1109/icdcs57875.2023.00013
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发表时间：
2023-07
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影响因子：
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通讯作者：
Han Zheng;Mengjing Liu;Fan Ye;Yuanyuan Yang
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文献类型：
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作者：
Han Zheng;Mengjing Liu;Fan Ye;Yuanyuan Yang
完全分散的道路车辆模型培训可以利用众包数据，而不依赖于中央服务器、基础设施或互联网覆盖。然而，在无线通信不可靠和接触时间短的情况下，对等车辆之间的模型共享可能会遭受严重的损失，从而频繁发生故障。为了应对这些挑战，我们提出了RoADTrain，这是一种路径辅助的分散式对等模型训练方法，它谨慎地选择成功共享模型机会较高的车辆。它限制了每轮通信时间，但在车辆机动性和通信不可靠的情况下保持了模型的性能。基于共享的路径信息，连接的车辆集群可以估计链路可靠性和接触持续时间信息，并将其嵌入到通信拓扑中。我们将拓扑分解为支持并行通信的子图，并找出其中具有最高代数连通性的子集，从而最大限度地提高模型共享成功率的簇中的信息流速度，从而加速簇中的模型训练。我们使用流行的CALA模拟器对驾驶决策模型进行了广泛的评估。RoADTrain实现了相当的驾驶成功率，比通常在模型共享中成功的典型分散学习方法(如SGP)的收敛速度快1.2-4.5倍，并显著超过在最艰难的驾驶条件下考虑损失17%-27%的其他基准测试。这些表明，路线共享能够精明地选择模型共享的车辆，从而在无线损耗和移动性方面实现更好的模型性能和更快的融合。
Fully decentralized model training for on-road vehicles can leverage crowdsourced data while not depending on central servers, infrastructure or Internet coverage. However, under unreliable wireless communication and short contact duration, model sharing among peer vehicles may suffer severe losses thus fail frequently. To address these challenges, we propose “RoADTrain”, a route-assisted decentralized peer model training approach that carefully chooses vehicles with high chances of successful model sharing. It bounds the per round communication time yet retains model performance under vehicle mobility and unreliable communication. Based on shared route information, a connected cluster of vehicles can estimate and embed the link reliability and contact duration information into the communication topology. We decompose the topology into subgraphs supporting parallel communication, and identify a subset of them with the highest algebraic connectivity that can maximize the speed of the information flow in the cluster with high model sharing successes, thus accelerating model training in the cluster. We conduct extensive evaluation on driving decision making models using the popular CARLA simulator. RoADTrain achieves comparable driving success rates and 1.2–4.5× faster convergence than representative decentralized learning methods that always succeed in model sharing (e.g., SGP), and significantly outperforms other benchmarks that consider losses by 17–27% in the hardest driving conditions. These demonstrate that route sharing enables shrewd selection of vehicles for model sharing, thus better model performance and faster convergence against wireless losses and mobility.