Federated learning (FL) facilitates a privacy-preserving neural network
training paradigm through collaboration between edge clients and a central
server. One significant challenge is that the distributed data is not
independently and identically distributed (non-IID), typically including both
intra-domain and inter-domain heterogeneity. However, recent research is
limited to simply using averaged signals as a form of regularization and only
focusing on one aspect of these non-IID challenges. Given these limitations,
this paper clarifies these two non-IID challenges and attempts to introduce
cluster representation to address them from both local and global perspectives.
Specifically, we propose a dual-clustered feature contrast-based FL framework
with dual focuses. First, we employ clustering on the local representations of
each client, aiming to capture intra-class information based on these local
clusters at a high level of granularity. Then, we facilitate cross-client
knowledge sharing by pulling the local representation closer to clusters shared
by clients with similar semantics while pushing them away from clusters with
dissimilar semantics. Second, since the sizes of local clusters belonging to
the same class may differ for each client, we further utilize clustering on the
global side and conduct averaging to create a consistent global signal for
guiding each local training in a contrastive manner. Experimental results on
multiple datasets demonstrate that our proposal achieves comparable or superior
performance gain under intra-domain and inter-domain heterogeneity.

通过引入聚类表示、局部聚类和全局信号等方法，本研究提出了一个双聚类特征对比的联邦学习框架，旨在解决非独立同分布数据的挑战，以跨客户知识共享和一致的全局指导为重点，实验结果表明，在域内和域间异质性下，该框架取得了可比或更高的性能提升。

FedCCL：基于领域异质性的联邦双聚类特征对比

FedCCL: Federated Dual-Clustered Feature Contrast Under Domain  Heterogeneity

Aligning signals from different modalities is an important step in
vision-language representation learning as it affects the performance of later
stages such as cross-modality fusion. Since image and text typically reside in
different regions of the feature space, directly aligning them at instance
level is challenging especially when features are still evolving during
training. In this paper, we propose to align at a higher and more stable level
using cluster representation. Specifically, we treat image and text as two
"views" of the same entity, and encode them into a joint vision-language coding
space spanned by a dictionary of cluster centers (codebook). We contrast
positive and negative samples via their cluster assignments while
simultaneously optimizing the cluster centers. To further smooth out the
learning process, we adopt a teacher-student distillation paradigm, where the
momentum teacher of one view guides the student learning of the other. We
evaluated our approach on common vision language benchmarks and obtain new SoTA
on zero-shot cross modality retrieval while being competitive on various other
transfer tasks.

该研究论文提出了一种基于聚类表示学习的多模态信号对齐方法，使用字典聚类编码将图像和文本编码到一个共同的编码空间中，并采用师生蒸馏的方法进一步优化学习过程，在多种视觉语言基准测试中取得了最新的最佳结果。

使用表示编码书进行多模态对齐

Multi-modal Alignment using Representation Codebook

Multiplex networks are complex graph structures in which a set of entities
are connected to each other via multiple types of relations, each relation
representing a distinct layer. Such graphs are used to investigate many complex
biological, social, and technological systems. In this work, we present a novel
semi-supervised approach for structure-aware representation learning on
multiplex networks. Our approach relies on maximizing the mutual information
between local node-wise patch representations and label correlated
structure-aware global graph representations to model the nodes and cluster
structures jointly. Specifically, it leverages a novel cluster-aware,
node-contextualized global graph summary generation strategy for effective
joint-modeling of node and cluster representations across the layers of a
multiplex network. Empirically, we demonstrate that the proposed architecture
outperforms state-of-the-art methods in a range of tasks: classification,
clustering, visualization, and similarity search on seven real-world multiplex
networks for various experiment settings.

本研究提出了一种新的半监督结构感知表征学习方法，通过最大化局部节点表示和标签相关的全局图表示之间的互信息来联合建模节点和群集结构，并采用聚类感知、节点上下文全局图概括生成策略来有效地联合建模多层复合网络中的节点和群集表示，实验结果表明，提出的方法在分类、聚类、可视化和相似性搜索等任务中优于现有方法。