基于Stackelberg博弈的多平台移动边缘学习中学习者激励研究
Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach
摘要：
移动边缘学习（MEL）是一种学习范式，其使得能够在异构边缘设备（例如，IoT设备）。多协调器MEL是指多个学习任务与不同数据集的共存，每个任务都由一个协调器管理，以促进分布式训练过程。在MEL中，如果没有足够的训练数据或计算资源，训练性能就会恶化。因此，激励边缘设备成为学习者并提供其计算资源，以及提供其私有数据或从编排器接收所需数据并参与学习任务的训练过程至关重要。在这项工作中，我们提出了一个激励机制，我们制定了一个2轮Stackelberg游戏，以激励参与的学习者的管弦乐队和学习者的互动。在第一轮中，学习者决定参与哪个学习任务，然后在第二轮中，训练参数和数据量用于参与的情况下的训练，使得它们的效用最大化。然后，我们分析研究的培训轮，并得出学习者的最佳策略。最后，数值实验已经进行了评估所提出的激励机制的性能。
英文摘要：
Mobile Edge Learning (MEL) is a learning paradigm that enables distributed training of Machine Learning models over heterogeneous edge devices (e.g., IoT devices). Multi-orchestrator MEL refers to the coexistence of multiple learning tasks with different datasets, each of which being governed by an orchestrator to facilitate the distributed training process. In MEL, the training performance deteriorates without the availability of sufficient training data or computing resources. Therefore, it is crucial to motivate edge devices to become learners and offer their computing resources, and either offer their private data or receive the needed data from the orchestrator and participate in the training process of a learning task. In this work, we propose an incentive mechanism, where we formulate the orchestrators-learners interactions as a 2-round Stackelberg game to motivate the participation of the learners. In the first round, the learners decide which learning task to get engaged in, and then in the second round, the training parameters and the amount of data for training in case of participation such that their utility is maximized. We then study the training round analytically and derive the learners' optimal strategy. Finally, numerical experiments have been conducted to evaluate the performance of the proposed incentive mechanism.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
