发表机构
ETH Zurich; Bosch Research（苏黎世联邦理工学院; 博世研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对边缘设备增量学习带宽受限问题，提出MECAIL方法，通过14.6 KB小型专家网络适配基础模型，在D-RICO和ODinW-13上达到与参数密集方法相当的性能，实现高效大规模部署。
AI 中文摘要
智能交通系统需要增量学习（IL）以在动态环境中持续提升整体性能。然而，大多数边缘设备缺乏支持设备端增量学习的计算资源，因此需要从中心服务器传输更新。我们提出利用这一设置来获得密集的、专门的模块覆盖，将固定的基础模型适配到特定的时空场景，如停车场、加油站、渡轮码头或建筑工地。然而，为了通过V2X、Wi-Fi和2G-5G硬件上的TCP、UDP和BTP可靠地传输这些模块到边缘设备，我们严格限制每个模块为14.6 KB，以适应第一个TCP窗口并最小化UDP/BTP分片。我们进一步引入了通信感知增量学习的专家混合（MECAIL），这是第一个满足这一严格要求的方法，其中每个新领域或环境由一个适配基础模型的小型专家网络提供服务。我们在D-RICO和ODinW-13上验证了MECAIL，它在很大程度上匹配了参数密集型方法的性能，同时实现了实用的、带宽高效的大规模部署。这使得专家能够全面覆盖高度特定、聚焦和临时的情况。
英文摘要
Intelligent transportation systems require Incremental Learning (IL) to continually improve their overall performance in dynamic environments. However, most edge devices lack the computational resources to support on-device IL, requiring updates to be transmitted from centralized servers. We propose using this setup to obtain dense, specialized module coverage that adapts a fixed base model to specific spatiotemporal contexts, such as parking lots, gas stations, ferries, or construction sites. However, in order to reliably transmit these modules to the edge device, using TCP, UDP, and BTP over V2X, Wi-Fi, and 2G-5G hardware, we establish a strict limit of 14.6 KB per module to fit within the first TCP window and to minimize UDP/BTP fragmentation. We further introduce Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model. We validate MECAIL on D-RICO and ODinW-13, where it largely matches the performance of parameter-heavy approaches while enabling practical, bandwidth-efficient large-scale deployment. This allows comprehensive coverage by experts for highly specific, focused, and temporary situations.
CommentsAccepted at ITSC 2026
