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.

通过控制数据采样过程，我们可以减轻过拟合问题并提高整体准确性，从而为具有实时感知能力的联邦学习系统中的资源分配提供了有希望的解决方案。