解决边缘联邦学习中资源受限下的客户端调度与资源分配问题。
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
- 基于李雅普诺夫优化的在线调度策略，无需未来系统信息。
- 显著降低训练延迟，提升资源利用效率。
- 适合部署在资源受限的移动边缘网络场景。
联邦学习（FL）使边缘设备可在不共享原始数据的情况下协同训练模型，因其保护隐私的优势已在诸多实际应用中部署。然而，在计算、带宽和能量受限的移动边缘网络中，由于数据和系统异构性，联邦学习常面临高训练延迟和低模型准确率的问题。本文研究在资源约束与不确定性下，如何优化客户端调度与资源分配以最小化训练延迟并保持模型精度。首先分析客户端采样对模型收敛的影响，构建了考虑运行时间与模型性能权衡的随机优化问题。为求解该问题，提出一种基于李雅普诺夫优化的在线控制方案，无需预先知晓未来系统动态。大量实验表明，所提方案相比现有方法能有效降低训练延迟并提升资源效率。
原文摘要 · Abstract (English)
Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been deployed in many real-world applications. However, deploying FL over mobile edge networks with constrained resources such as power, bandwidth, and computation suffers from high training latency and low model accuracy, particularly under data and system heterogeneity. In this paper, we investigate the optimal client scheduling and resource allocation for FL over mobile edge networks under resource constraints and uncertainty to minimize the training latency while maintaining the model accuracy. Specifically, we first analyze the impact of client sampling on model convergence in FL and formulate a stochastic optimization problem that captures the trade-off between the running time and model performance under heterogeneous and uncertain system resources. To solve the formulated problem, we further develop an online control scheme based on Lyapunov-based optimization for client sampling and resource allocation without requiring the knowledge of future dynamics in the FL system. Extensive experimental results demonstrate that the proposed scheme can improve both the training latency and resource efficiency compared with the existing schemes.
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