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

Incivility remains a major challenge for online discussion platforms, to such
an extent that even conversations between well-intentioned users can often
derail into uncivil behavior. Traditionally, platforms have relied on
moderators to -- with or without algorithmic assistance -- take corrective
actions such as removing comments or banning users. In this work we propose a
complementary paradigm that directly empowers users by proactively enhancing
their awareness about existing tension in the conversation they are engaging in
and actively guides them as they are drafting their replies to avoid further
escalation.
As a proof of concept for this paradigm, we design an algorithmic tool that
provides such proactive information directly to users, and conduct a user study
in a popular discussion platform. Through a mixed methods approach combining
surveys with a randomized controlled experiment, we uncover qualitative and
quantitative insights regarding how the participants utilize and react to this
information. Most participants report finding this proactive paradigm valuable,
noting that it helps them to identify tension that they may have otherwise
missed and prompts them to further reflect on their own replies and to revise
them. These effects are corroborated by a comparison of how the participants
draft their reply when our tool warns them that their conversation is at risk
of derailing into uncivil behavior versus in a control condition where the tool
is disabled. These preliminary findings highlight the potential of this
user-centered paradigm and point to concrete directions for future
implementations.

通过设计算法工具来提高用户对话中紧张氛围的意识并引导其积极回应，我们提出了一种不同于传统平台依靠管理员干预的方法。通过用户实验，我们发现这种积极主动的用户中心模式受到用户的欢迎，并可能在未来得到广泛应用。