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.

翻译：物联网在许多场景中需要智能决策。为此，可以利用单个节点上可用于感知或计算（或两者兼有）的资源。这分别催生了被称为参与式感知和联邦学习的方法。我们研究通过一种分布式方法同时实现这两者，该方法基于赋予本地节点博弈论决策能力。全局能量最小化目标与单个节点在多个学习轮次中优化本地感知和数据传输开销相结合。我们基于理论框架和采用真实数据的模拟网络场景实验，对该技术进行了广泛评估。这种分布式方法可以在无需数据收集器集中监督的情况下，为联邦学习达到期望的准确度水平。然而，根据赋予单个节点本地成本的权重，它也可能导致显著较高的无政府状态代价（从1.28起）。因此，我们认为需要建立激励机制，该机制可能基于单个节点的信息年龄。