针对边缘计算中多任务联邦学习的依赖关系，提出聚类选客户端框架以加速训练。
Cluster-Based Client Selection for Dependent Multi-Task Federated Learning in Edge Computing
- 按客户端数据分布聚类，用地球移动距离优化选人策略。
- 实验显示收敛更快、通信与计算成本更低，准确率更高。
- 适合异构边缘环境中的多任务协同学习场景。
在移动边缘计算（MEC）环境中，研究联邦学习（FL）下的客户端选择问题，尤其在存在任务依赖关系的多任务设置下，旨在降低完成各类学习任务所需的总时间。我们提出 CoDa-FL——一种面向聚类且考虑依赖关系的框架，通过基于聚类的客户端选择和依赖任务分配来减少总耗时。该方法利用地球移动距离（EMD）根据本地数据分布进行客户端聚类，以降低计算开销并提升通信效率。我们推导出簇内 EMD 与收敛所需训练轮数之间的直接显式关系，从而简化了最优解求解过程。此外，引入基于有向无环图的任务调度机制，有效管理任务依赖。数值实验表明，与现有基准相比，所提 CoDa-FL 在异构 MEC 环境下实现了更快速收敛、更低通信与计算成本，以及更高的学习精度。
原文摘要 · Abstract (English)
We study the client selection problem in Federated Learning (FL) within mobile edge computing (MEC) environments, particularly under the dependent multi-task settings, to reduce the total time required to complete various learning tasks. We propose CoDa-FL, a Cluster-oriented and Dependency-aware framework designed to reduce the total required time via cluster-based client selection and dependent task assignment. Our approach considers Earth Mover's Distance (EMD) for client clustering based on their local data distributions to lower computational cost and improve communication efficiency. We derive a direct and explicit relationship between intra-cluster EMD and the number of training rounds required for convergence, thereby simplifying the otherwise complex process of obtaining the optimal solution. Additionally, we incorporate a directed acyclic graph-based task scheduling mechanism to effectively manage task dependencies. Through numerical experiments, we validate that our proposed CoDa-FL outperforms existing benchmarks by achieving faster convergence, lower communication and computational costs, and higher learning accuracy under heterogeneous MEC settings.
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