Federated multi-view clustering offers the potential to develop a global
clustering model using data distributed across multiple devices. However,
current methods face challenges due to the absence of label information and the
paramount importance of data privacy. A significant issue is the feature
heterogeneity across multi-view data, which complicates the effective mining of
complementary clustering information. Additionally, the inherent incompleteness
of multi-view data in a distributed setting can further complicate the
clustering process. To address these challenges, we introduce a federated
incomplete multi-view clustering framework with heterogeneous graph neural
networks (FIM-GNNs). In the proposed FIM-GNNs, autoencoders built on
heterogeneous graph neural network models are employed for feature extraction
of multi-view data at each client site. At the server level, heterogeneous
features from overlapping samples of each client are aggregated into a global
feature representation. Global pseudo-labels are generated at the server to
enhance the handling of incomplete view data, where these labels serve as a
guide for integrating and refining the clustering process across different data
views. Comprehensive experiments have been conducted on public benchmark
datasets to verify the performance of the proposed FIM-GNNs in comparison with
state-of-the-art algorithms.

通过引入具有异质图神经网络的不完整多视图聚类框架（FIM-GNNs），我们解决了由于缺乏标签信息和数据隐私的重要性而导致的异构特征数据的有效挖掘困难，以及分布式环境中多视图数据的固有不完整性使得聚类过程复杂化的问题。

异构图神经网络下的联邦多视角聚类

Federated Incomplete Multi-View Clustering with Heterogeneous Graph  Neural Networks

Score-based generative models (or diffusion models for short) have proven
successful across many domains in generating text and image data. However, the
consideration of mixed-type tabular data with this model family has fallen
short so far. Existing research mainly combines different diffusion processes
without explicitly accounting for the feature heterogeneity inherent to tabular
data. In this paper, we combine score matching and score interpolation to
ensure a common type of continuous noise distribution that affects both
continuous and categorical features alike. Further, we investigate the impact
of distinct noise schedules per feature or per data type. We allow for
adaptive, learnable noise schedules to ensure optimally allocated model
capacity and balanced generative capability. Results show that our model
consistently outperforms state-of-the-art benchmark models and that accounting
for heterogeneity within the noise schedule design boosts the sample quality.

通过结合评分匹配和评分插值，我们在这篇论文中提出了一种针对混合类型表格数据的得分生成模型。模型考虑了特征异质性，并允许自适应、可学习的噪声调度以提高样本质量。实验结果表明，我们的模型在质量上始终优于最先进的基准模型，并且噪声调度设计中的异质性考虑提升了样本质量。

混合类型表格数据的连续扩散

Continuous Diffusion for Mixed-Type Tabular Data

Multiple medical institutions collaboratively training a model using
federated learning (FL) has become a promising solution for maximizing the
potential of data-driven models, yet the non-independent and identically
distributed (non-iid) data in medical images is still an outstanding challenge
in real-world practice. The feature heterogeneity caused by diverse scanners or
protocols introduces a drift in the learning process, in both local (client)
and global (server) optimizations, which harms the convergence as well as model
performance. Many previous works have attempted to address the non-iid issue by
tackling the drift locally or globally, but how to jointly solve the two
essentially coupled drifts is still unclear. In this work, we concentrate on
handling both local and global drifts and introduce a new harmonizing framework
called HarmoFL. First, we propose to mitigate the local update drift by
normalizing amplitudes of images transformed into the frequency domain to mimic
a unified imaging setting, in order to generate a harmonized feature space
across local clients. Second, based on harmonized features, we design a client
weight perturbation guiding each local model to reach a flat optimum, where a
neighborhood area of the local optimal solution has a uniformly low loss.
Without any extra communication cost, the perturbation assists the global model
to optimize towards a converged optimal solution by aggregating several local
flat optima. We have theoretically analyzed the proposed method and empirically
conducted extensive experiments on three medical image classification and
segmentation tasks, showing that HarmoFL outperforms a set of recent
state-of-the-art methods with promising convergence behavior. Code is available
at this https URL

本研究提出了一种名为 HarmoFL 的新框架，旨在解决医学图像中的飘移问题，通过归一化特征空间和设计客户端权重扰动，以改善本地更新漂移并实现全局最优解的聚合，来达到更好的收敛表现。