The paradigm of vertical federated learning (VFL), where institutions
collaboratively train machine learning models via combining each other's local
feature or label information, has achieved great success in applications to
financial risk management (FRM). The surging developments of graph
representation learning (GRL) have opened up new opportunities for FRM
applications under FL via efficiently utilizing the graph-structured data
generated from underlying transaction networks. Meanwhile, transaction
information is often considered highly sensitive. To prevent data leakage
during training, it is critical to develop FL protocols with formal privacy
guarantees. In this paper, we present an end-to-end GRL framework in the VFL
setting called VESPER, which is built upon a general privatization scheme
termed perturbed message passing (PMP) that allows the privatization of many
popular graph neural architectures.Based on PMP, we discuss the strengths and
weaknesses of specific design choices of concrete graph neural architectures
and provide solutions and improvements for both dense and sparse graphs.
Extensive empirical evaluations over both public datasets and an industry
dataset demonstrate that VESPER is capable of training high-performance GNN
models over both sparse and dense graphs under reasonable privacy budgets.

本文介绍了一种基于 VFL 框架的端到端 GRL 方法，称为 VESPER，它建立在一种通用的隐私保护方案（PMP）上，允许对许多常见的图神经网络架构进行隐私保护。通过对具体图神经网络架构的设计选择进行讨论，我们提出了针对稠密图和稀疏图的解决方案和改进方法。对公共数据集和行业数据集的大量实证评估表明，VESPER 能够在合理的隐私预算下训练高性能的 GNN 模型。

图神经网络的隐私保护设计及其在纵向联邦学习中的应用

Privacy-preserving design of graph neural networks with applications to  vertical federated learning

Deep neural networks for graphs have emerged as a powerful tool for learning
on complex non-euclidean data, which is becoming increasingly common for a
variety of different applications. Yet, although their potential has been
widely recognised in the machine learning community, graph learning is largely
unexplored for downstream tasks such as robotics applications. To fully unlock
their potential, hence, we propose a review of graph neural architectures from
a robotics perspective. The paper covers the fundamentals of graph-based
models, including their architecture, training procedures, and applications. It
also discusses recent advancements and challenges that arise in applied
settings, related for example to the integration of perception,
decision-making, and control. Finally, the paper provides an extensive review
of various robotic applications that benefit from learning on graph structures,
such as bodies and contacts modelling, robotic manipulation, action
recognition, fleet motion planning, and many more. This survey aims to provide
readers with a thorough understanding of the capabilities and limitations of
graph neural architectures in robotics, and to highlight potential avenues for
future research.

在这篇论文中，通过对图神经结构进行综述，从机器人的角度探索了图学习在机器人应用中的潜力、挑战和各种应用案例，以期为读者提供深入的了解，并为未来的研究指明可能的方向。