In the domain of Federated Learning (FL) systems, recent cutting-edge methods heavily rely on ideal conditions convergence analysis. Specifically, these approaches assume that the training datasets on IoT devices possess similar attributes to the global data distribution. However, this approach fails to capture the full spectrum of data characteristics in real-time sensing FL systems. In order to overcome this limitation, we suggest a new approach system specifically designed for IoT networks with real-time sensing capabilities. Our approach takes into account the generalization gap due to the user's data sampling process. By effectively controlling this sampling process, we can mitigate the overfitting issue and improve overall accuracy. In particular, We first formulate an optimization problem that harnesses the sampling process to concurrently reduce overfitting while maximizing accuracy. In pursuit of this objective, our surrogate optimization problem is adept at handling energy efficiency while optimizing the accuracy with high generalization. To solve the optimization problem with high complexity, we introduce an online reinforcement learning algorithm, named Sample-driven Control for Federated Learning (SCFL) built on the Soft Actor-Critic (A2C) framework. This enables the agent to dynamically adapt and find the global optima even in changing environments. By leveraging the capabilities of SCFL, our system offers a promising solution for resource allocation in FL systems with real-time sensing capabilities.

翻译：在联邦学习（FL）系统领域，近期前沿方法严重依赖于理想条件下的收敛性分析。具体而言，这些方法假设物联网设备上的训练数据集具有与全局数据分布相似的属性。然而，这种假设未能捕捉实时感知FL系统中数据的全部特征。为克服此局限性，我们提出一种专为具备实时感知能力的物联网网络设计的新方法。该方法考虑了因用户数据采样过程导致的泛化差距。通过有效控制采样过程，我们能够缓解过拟合问题并提升整体精度。具体而言，我们首先构建一个优化问题，利用采样过程在减少过拟合的同时最大化精度。为实现此目标，我们的替代优化问题在优化精度的同时兼顾高泛化能力与能效。针对高复杂度的优化求解，我们引入一种基于软演员-评论家（Soft Actor-Critic, A2C）框架的在线强化学习算法——样本驱动联邦学习控制（SCFL）。该算法使智能体能够动态适应环境变化并寻找全局最优解。通过发挥SCFL的能力，我们的系统为具备实时感知能力的FL系统中的资源分配提供了有前景的解决方案。