arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~

FedSceneX:面向同场景多模态联邦边缘学习的目标时间编排

FedSceneX: Time-to-Target Orchestration for Same-Scene Multimodal Federated Edge Learning

Dhe Yeong Tchalla, Beining Wu, Jun Huang, Shuyang Gu, Qiang Duan

arXiv 2608.07730首次发表:更新:

AI 中文总结

FedSceneX针对同场景多模态联邦边缘学习的轮次耗时不固定问题,提出VHP优化方法,在nuScenes基准测试中缩短每轮次活跃时间,20小时预算内准确率最高且保持全部四种模态。

AI 中文摘要

传感边缘的联邦学习通常以通信轮次进行评估,但单轮次并不对应固定工作量。即便使用相同硬件,所对比方法每轮次耗时为3.3至9.8小时,基于轮次的对比具有误导性。对于同场景多模态客户端,该问题更为突出,因为相机、视频、激光雷达(LiDAR)和雷达的训练与通信成本差异显著,而现有方法将每轮次的模态构成视为固定值。为解决此问题,我们提出FedSceneX,这一编排器可联合确定轮次构成,以最大化每活跃小时的学习价值。其优化方法为每小时价值定价(Value-per-Hour Pricing, VHP),该方法通过参数变换将分数目标转化,并对偶化上行链路约束,得到闭式客户端价格,其权重捕捉资源影子成本。基于这些价格,FedSceneX在模态覆盖约束下选择客户端,通过反向注水分配精度,并将更新分配给边缘服务器。在包含15个客户端和12个基线的完整nuScenes基准测试中,FedSceneX将每轮次的活跃时间缩短至3.31小时,而基线方法的该值为4.85至9.78小时。在所有随机种子下,FedSceneX在20小时预算内保持全部四种模态的同时达到最高准确率,其优势在10至45小时范围内持续存在,之后传统方法将超过它。

英文摘要

Federated learning at the sensing edge is typically evaluated by communication rounds, yet a round does not represent a fixed amount of work. Even on identical hardware, the methods we compare require 3.3 to 9.8 hours per round, which makes round-based comparisons misleading. The problem is more obvious for same-scene multimodal clients, since camera, video, LiDAR, and radar workloads differ substantially in training and communication cost, while existing methods treat the modality composition of each round as fixed. To address it, we introduce FedSceneX, an orchestrator that jointly determines round composition to maximize learning value per active hour. The optimization method, Value-per-Hour Pricing (VHP), converts the fractional objective through a parametric transformation and dualizes the uplink constraint, yielding a closed-form client price whose weights capture resource shadow costs. Based on these prices, FedSceneX selects clients subject to a modality coverage constraint, allocates precision through reverse water filling, and assigns updates to edge servers. On the full nuScenes benchmark with fifteen clients and twelve baselines, FedSceneX reduces the active time per round to 3.31 hours, compared with 4.85 to 9.78 hours for the baselines. Across all random seeds, it achieves the highest accuracy within a twenty-hour budget while preserving all four modalities. Its advantage persists from ten to forty-five hours, after which conventional methods overtake it.

论文原文

arXiv 摘要页 · PDF 原文 · HTML 原文

↑