Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, possible coexistence of multiple learning tasks with different datasets may arise. The heterogeneity in edge devices' capabilities will require the joint optimization of the learners-orchestrator association and task allocation. To this end, we aim to develop an energy-efficient framework for learners-orchestrator association and learning task allocation, in which each orchestrator gets associated with a group of learners with the same learning task based on their communication channel qualities and computational resources, and allocate the tasks accordingly. Therein, a multi objective optimization problem is formulated to minimize the total energy consumption and maximize the learning tasks' accuracy. However, solving such optimization problem requires centralization and the presence of the whole environment information at a single entity, which becomes impractical in large-scale systems. To reduce the solution complexity and to enable solution decentralization, we propose lightweight heuristic algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity. Simulation results show that the proposed approaches reduce the energy consumption significantly while executing multiple learning tasks compared to recent state-of-the-art methods.

中文翻译：

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节能的多编排器移动边缘学习

移动边缘学习 (MEL) 是一种协作学习范式，其特点是在边缘设备（例如 IoT 设备）上对机器学习 (ML) 模型进行分布式训练。在 MEL 中，可能会出现多个学习任务与不同数据集的共存。边缘设备能力的异质性将需要学习者-协调者关联和任务分配的联合优化。为此，我们旨在为学习者-协调者关联和学习任务分配开发一个节能框架，其中每个协调者根据他们的通信渠道质量和计算资源与一组具有相同学习任务的学习者相关联，并且相应地分配任务。其中，制定了一个多目标优化问题，以最小化总能量消耗并最大化学习任务的准确性。然而，解决这样的优化问题需要将整个环境信息集中在一个实体中，这在大规模系统中变得不切实际。为了降低解决方案的复杂性并实现解决方案的分散化，我们提出了轻量级启发式算法，可以实现接近最佳的性能并促进能耗、准确性和解决方案复杂性之间的权衡。仿真结果表明，与最近的最先进方法相比，所提出的方法在执行多个学习任务时显着降低了能耗。解决这样的优化问题需要将整个环境信息集中在一个实体中，这在大规模系统中变得不切实际。为了降低解决方案的复杂性并实现解决方案的分散化，我们提出了轻量级启发式算法，可以实现接近最佳的性能并促进能耗、准确性和解决方案复杂性之间的权衡。仿真结果表明，与最近的最先进方法相比，所提出的方法在执行多个学习任务时显着降低了能耗。解决这样的优化问题需要将整个环境信息集中在一个实体中，这在大规模系统中变得不切实际。为了降低解决方案的复杂性并实现解决方案的分散化，我们提出了轻量级启发式算法，可以实现接近最佳的性能并促进能耗、准确性和解决方案复杂性之间的权衡。仿真结果表明，与最近的最先进方法相比，所提出的方法在执行多个学习任务时显着降低了能耗。我们提出了轻量级启发式算法，可以实现接近最佳的性能并促进能耗、准确性和解决方案复杂性之间的权衡。仿真结果表明，与最近的最先进方法相比，所提出的方法在执行多个学习任务时显着降低了能耗。我们提出了轻量级启发式算法，可以实现接近最佳的性能并促进能耗、准确性和解决方案复杂性之间的权衡。仿真结果表明，与最近的最先进方法相比，所提出的方法在执行多个学习任务时显着降低了能耗。