Federated learning learns from scattered data by fusing collaborative models
from local nodes. However, due to chaotic information distribution, the model
fusion may suffer from structural misalignment with regard to unmatched
parameters. In this work, we propose a novel federated learning framework to
resolve this issue by establishing a firm structure-information alignment
across collaborative models. Specifically, we design a feature-oriented
regulation method ({$\Psi$-Net}) to ensure explicit feature information
allocation in different neural network structures. Applying this regulating
method to collaborative models, matchable structures with similar feature
information can be initialized at the very early training stage. During the
federated learning process under either IID or non-IID scenarios, dedicated
collaboration schemes further guarantee ordered information distribution with
definite structure matching, so as the comprehensive model alignment.
Eventually, this framework effectively enhances the federated learning
applicability to extensive heterogeneous settings, while providing excellent
convergence speed, accuracy, and computation/communication efficiency.

本文提出了一种新颖的联邦学习框架，通过设计特征定向调节方法 (ψ-Net) 来确保显式特征信息在不同的神经网络结构中的分配，并应用于协作模型中，在通常分散和混乱的信息环境下，通过协定的时序，信息的有序分配及结构匹配，提高了联邦学习的适用性，同时提供了优秀的收敛速度、精度和计算 / 通信效率。

异构联邦学习

Heterogeneous Federated Learning

We apply entropy agglomeration (EA), a recently introduced algorithm, to
cluster the words of a literary text. EA is a greedy agglomerative procedure
that minimizes projection entropy (PE), a function that can quantify the
segmentedness of an element set. To apply it, the text is reduced to a feature
allocation, a combinatorial object to represent the word occurences in the
text's paragraphs. The experiment results demonstrate that EA, despite its
reduction and simplicity, is useful in capturing significant relationships
among the words in the text. This procedure was implemented in Python and
published as a free software: REBUS.

应用熵聚类算法对文学文本进行聚类分析，实验结果表明该算法具有在捕捉文本中单词之间显著关系方面的实用性。

通过投影熵聚类单词

Clustering Words by Projection Entropy

Infinite mixture models are commonly used for clustering. One can sample from
the posterior of mixture assignments by Monte Carlo methods or find its maximum
a posteriori solution by optimization. However, in some problems the posterior
is diffuse and it is hard to interpret the sampled partitionings. In this
paper, we introduce novel statistics based on block sizes for representing
sample sets of partitionings and feature allocations. We develop an
element-based definition of entropy to quantify segmentation among their
elements. Then we propose a simple algorithm called entropy agglomeration (EA)
to summarize and visualize this information. Experiments on various infinite
mixture posteriors as well as a feature allocation dataset demonstrate that the
proposed statistics are useful in practice.

本文提出了一种基于区块大小的统计量，并且采用基于元素的熵的定义来量化其分割信息，并且使用熵聚类算法简化和可视化信息，实验证明这种统计量在实践中非常有用，使用于各种无限混合后验及特征分配数据集。

分区和特征分配的摘要统计

Summary Statistics for Partitionings and Feature Allocations

The problem of inferring a clustering of a data set has been the subject of
much research in Bayesian analysis, and there currently exists a solid
mathematical foundation for Bayesian approaches to clustering. In particular,
the class of probability distributions over partitions of a data set has been
characterized in a number of ways, including via exchangeable partition
probability functions (EPPFs) and the Kingman paintbox. Here, we develop a
generalization of the clustering problem, called feature allocation, where we
allow each data point to belong to an arbitrary, non-negative integer number of
groups, now called features or topics. We define and study an "exchangeable
feature probability function" (EFPF)---analogous to the EPPF in the clustering
setting---for certain types of feature models. Moreover, we introduce a
"feature paintbox" characterization---analogous to the Kingman paintbox for
clustering---of the class of exchangeable feature models. We provide a further
characterization of the subclass of feature allocations that have EFPF
representations.

本文介绍了一种名为特征分配的聚类问题的推广形式，其中允许每个数据点属于任意数量个组，通过引入交换特征概率函数和特征画盒描述这一问题，并提出了具有 EFPF 表示的特征分配子类。