标题： 协同内容审核在Fediverse中 显示英文标题

标题： Collaborative Content Moderation in the Fediverse

摘要： 联邦宇宙，由提供各种互操作服务的互联服务器组成（例如Mastodon中的微型博客），已迅速走红。 这种突然的增长，部分是由埃隆·马斯克收购推特所推动的，但也给管理员带来了挑战。 本文关注的一个特定挑战是内容审核，例如删除垃圾信息或仇恨言论的需求。 虽然像Facebook和推特这样的集中式平台依赖自动化工具进行审核，但它们对大量标记数据和专业基础设施的依赖，使得它们在像联邦宇宙这样的去中心化、低资源环境中不切实际。 在这项工作中，我们设计并评估了FedMod，这是一个基于联邦学习的合作内容审核系统。 我们的系统使服务器能够与具有相似服务器交换部分训练的本地内容审核模型的参数，创建一个由合作服务器共享的联邦模型。 FedMod在三个不同的内容审核任务中表现出稳健的性能：有害内容检测、机器人内容检测和内容警告分配，分别实现了平均每个服务器的宏F1分数为0.71、0.73和0.58。 显示英文摘要

摘要： The Fediverse, a group of interconnected servers providing a variety of interoperable services (e.g. micro-blogging in Mastodon) has gained rapid popularity. This sudden growth, partly driven by Elon Musk's acquisition of Twitter, has created challenges for administrators though. This paper focuses on one particular challenge: content moderation, e.g. the need to remove spam or hate speech. While centralized platforms like Facebook and Twitter rely on automated tools for moderation, their dependence on massive labeled datasets and specialized infrastructure renders them impractical for decentralized, low-resource settings like the Fediverse. In this work, we design and evaluate FedMod, a collaborative content moderation system based on federated learning. Our system enables servers to exchange parameters of partially trained local content moderation models with similar servers, creating a federated model shared among collaborating servers. FedMod demonstrates robust performance on three different content moderation tasks: harmful content detection, bot content detection, and content warning assignment, achieving average per-server macro-F1 scores of 0.71, 0.73, and 0.58, respectively.