博弈论驱动物联网联邦学习，降低能耗并避免效率恶化
Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory
- 用博弈论让节点自主决策，兼顾本地能耗与全局精度
- 无需中心监管即可达成目标精度，但可能引发1.28以上的效率损失
- 适合关注低功耗边缘计算的系统设计者
物联网在诸多场景中需要智能决策。通过利用节点自身的感知或计算资源，可实现参与式感知与联邦学习。本文研究两者协同实现的分布式方法，基于博弈论赋予本地节点决策权，以最小化全局能耗为目标，并优化多轮学习中节点的本地感知与数据传输成本。通过理论分析与真实数据模拟实验进行广泛评估，该方法可在无中心监督下达到期望的联邦学习精度。然而，当节点局部成本权重过高时，可能导致价格的荒谬性（Price of Anarchy）从1.28起显著上升。因此，需引入激励机制，例如基于单个节点的信息年龄（Age of Information）。
原文摘要 · Abstract (English)
The Internet of Things requires intelligent decision making in many scenarios. To this end, resources available at the individual nodes for sensing or computing, or both, can be leveraged. This results in approaches known as participatory sensing and federated learning, respectively. We investigate the simultaneous implementation of both, through a distributed approach based on empowering local nodes with game theoretic decision making. A global objective of energy minimization is combined with the individual node's optimization of local expenditure for sensing and transmitting data over multiple learning rounds. We present extensive evaluations of this technique, based on both a theoretical framework and experiments in a simulated network scenario with real data. Such a distributed approach can reach a desired level of accuracy for federated learning without a centralized supervision of the data collector. However, depending on the weight attributed to the local costs of the single node, it may also result in a significantly high Price of Anarchy (from 1.28 onwards). Thus, we argue for the need of incentive mechanisms, possibly based on Age of Information of the single nodes.
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