In response to the increasing volume and sensitivity of data, traditional
centralized computing models face challenges, such as data security breaches
and regulatory hurdles. Federated Computing (FC) addresses these concerns by
enabling collaborative processing without compromising individual data privacy.
This is achieved through a decentralized network of devices, each retaining
control over its data, while participating in collective computations. The
motivation behind FC extends beyond technical considerations to encompass
societal implications. As the need for responsible AI and ethical data
practices intensifies, FC aligns with the principles of user empowerment and
data sovereignty. FC comprises of Federated Learning (FL) and Federated
Analytics (FA). FC systems became more complex over time and they currently
lack a clear definition and taxonomy describing its moving pieces. Current
surveys capture domain-specific FL use cases, describe individual components in
an FC pipeline individually or decoupled from each other, or provide a
quantitative overview of the number of published papers. This work surveys more
than 150 papers to distill the underlying structure of FC systems with their
basic building blocks, extensions, architecture, environment, and motivation.
We capture FL and FA systems individually and point out unique difference
between those two.

联邦计算是通过分散的设备网络实现合作处理而不损害个体数据隐私的一种方法，该研究调查了 150 多篇论文，总结了联邦计算系统的基本结构、扩展、架构、环境和动机，同时还强调了联邦学习和联邦分析之间的独特差异。

联邦计算 - 建模块、拓展与系统综述

Federated Computing -- Survey on Building Blocks, Extensions and Systems

In the realm of precision medicine, effective patient stratification and
disease subtyping demand innovative methodologies tailored for multi-omics
data. Clustering techniques applied to multi-omics data have become
instrumental in identifying distinct subgroups of patients, enabling a
finer-grained understanding of disease variability. This work establishes a
powerful framework for advancing precision medicine through unsupervised
random-forest-based clustering and federated computing. We introduce a novel
multi-omics clustering approach utilizing unsupervised random-forests. The
unsupervised nature of the random forest enables the determination of
cluster-specific feature importance, unraveling key molecular contributors to
distinct patient groups. Moreover, our methodology is designed for federated
execution, a crucial aspect in the medical domain where privacy concerns are
paramount. We have validated our approach on machine learning benchmark data
sets as well as on cancer data from The Cancer Genome Atlas (TCGA). Our method
is competitive with the state-of-the-art in terms of disease subtyping, but at
the same time substantially improves the cluster interpretability. Experiments
indicate that local clustering performance can be improved through federated
computing.

通过无监督随机森林聚类和联合计算，建立了一个强大的框架来推进精准医学，实现有效的患者分层和疾病亚型划分，改进了疾病亚型的解释性，并优化了局部聚类性能。