针对3D泛在计算中GPU设备的动态客户端选择难题，提出无需历史数据的概率化选法。
ProbSelect: Stochastic Client Selection for GPU-Accelerated Compute Devices in the 3D Continuum
- 基于解析建模与概率预测，不依赖历史数据或持续监控。
- 平均提升服务等级目标满足率13.77%，计算浪费降低72.5%。
- 适用于卫星、移动设备等动态场景下的GPU加速联邦学习系统。
将边缘、云与空间设备整合为统一的3D连续体，给联邦学习中的客户端选择带来重大挑战。传统方法依赖持续监控与历史数据收集，在卫星和移动设备频繁变动运行状态的动态环境中变得不切实际。此外，现有方案主要考虑基于CPU的计算，未能捕捉3D连续体中普遍存在的GPU加速训练的复杂特性。本文提出ProbSelect，一种利用解析建模与概率预测进行客户端选择的新方法，可在无需历史数据或持续监控的前提下，实现对用户定义的服务等级目标（SLO）的客户端选择。在多种GPU架构与工作负载上的广泛评估表明，ProbSelect在平均上使SLO合规性提升13.77%，同时相比基线方法减少72.5%的计算浪费。
原文摘要 · Abstract (English)
Integration of edge, cloud and space devices into a unified 3D continuum imposes significant challenges for client selection in federated learning systems. Traditional approaches rely on continuous monitoring and historical data collection, which becomes impractical in dynamic environments where satellites and mobile devices frequently change operational conditions. Furthermore, existing solutions primarily consider CPU-based computation, failing to capture complex characteristics of GPU-accelerated training that is prevalent across the 3D continuum. This paper introduces ProbSelect, a novel approach utilizing analytical modeling and probabilistic forecasting for client selection on GPU-accelerated devices, without requiring historical data or continuous monitoring. We model client selection within user-defined SLOs. Extensive evaluation across diverse GPU architectures and workloads demonstrates that ProbSelect improves SLO compliance by 13.77% on average while achieving 72.5% computational waste reduction compared to baseline approaches.
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