Federated training of Graph Neural Networks (GNN) has become popular in
recent years due to its ability to perform graph-related tasks under data
isolation scenarios while preserving data privacy. However, graph heterogeneity
issues in federated GNN systems continue to pose challenges. Existing
frameworks address the problem by representing local tasks using different
statistics and relating them through a simple aggregation mechanism. However,
these approaches suffer from limited efficiency from two aspects: low quality
of task-relatedness quantification and inefficacy of exploiting the
collaboration structure. To address these issues, we propose FedGKD, a novel
federated GNN framework that utilizes a novel client-side graph dataset
distillation method to extract task features that better describe
task-relatedness, and introduces a novel server-side aggregation mechanism that
is aware of the global collaboration structure. We conduct extensive
experiments on six real-world datasets of different scales, demonstrating our
framework's outperformance.

提出了 FedGKD，一种新的联邦图神经网络框架，利用客户端图数据集提取更好描述任务相关性的任务特征，并引入一种对全局协作结构感知的新型服务器端聚合机制，通过在六个不同规模的真实世界数据集上进行广泛实验，证明该框架的优越性。

FedGKD: 在联邦图神经网络中释放协作的力量

FedGKD: Unleashing the Power of Collaboration in Federated Graph Neural  Networks

In a decentralized household energy system comprised of various devices such
as home appliances, electric vehicles, and solar panels, end-users are able to
dig deeper into the system's details and further achieve energy sustainability
if they are presented with data on the electric energy consumption and
production at the granularity of the device. However, many databases in this
field are siloed from other domains, including solely information pertaining to
energy. This may result in the loss of information (e.g. weather) on each
device's energy use. Meanwhile, a large number of these datasets have been
extensively used in computational modeling techniques such as machine learning
models. While such computational approaches achieve great accuracy and
performance by concentrating only on a local view of datasets, model
reliability cannot be guaranteed since such models are very vulnerable to data
input fluctuations when information omission is taken into account. This
article tackles the data isolation issue in the field of smart energy systems
by examining Semantic Web methods on top of a household energy system. We offer
an ontology-based approach for managing decentralized data at the device-level
resolution in a system. As a consequence, the scope of the data associated with
each device may easily be expanded in an interoperable manner throughout the
Web, and additional information, such as weather, can be obtained from the Web,
provided that the data is organized according to W3C standards.

本文介绍了一个基于本体论的方法，用于在分散的家庭能源系统中管理分辨率为设备级别的数据，以便更好地利用来自互联网和 Web 的各种数据，促进能源可持续发展。