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。

基于样本驱动的联邦学习用于能效高和实时物联网传感

Sample-Driven Federated Learning for Energy-Efficient and Real-Time IoT  Sensing

Polynomial graph filters have been widely used as guiding principles in the
design of Graph Neural Networks (GNNs). Recently, the adaptive learning of the
polynomial graph filters has demonstrated promising performance for modeling
graph signals on both homophilic and heterophilic graphs, owning to their
flexibility and expressiveness. In this work, we conduct a novel preliminary
study to explore the potential and limitations of polynomial graph filter
learning approaches, revealing a severe overfitting issue. To improve the
effectiveness of polynomial graph filters, we propose Auto-Polynomial, a novel
and general automated polynomial graph filter learning framework that
efficiently learns better filters capable of adapting to various complex graph
signals. Comprehensive experiments and ablation studies demonstrate significant
and consistent performance improvements on both homophilic and heterophilic
graphs across multiple learning settings considering various labeling ratios,
which unleashes the potential of polynomial filter learning.

本文提出了一种新的自动多项式图滤波器学习框架 ——Auto-Polynomial，能够更高效地学习适应各种复杂图信号的滤波器，从而解决了多项式图滤波器学习中的过拟合问题，并在多个学习设置下，考虑各种标记比率，显著提高了同构和异构图的性能。