Collaborative Content Moderation in the Fediverse

2025-01-10

AI Summary

Resource-constrained servers in the decentralized Fediverse struggle to independently deploy efficient content moderation systems. Method: This paper proposes the first federated learning–based moderation framework designed for cross-server collaboration, enabling heterogeneous, low-resource nodes to jointly train models via local parameter exchange—without sharing raw data—while unifying detection of harmful content, bot-generated content, and content warnings. Contribution/Results: Experiments demonstrate average macro-F1 scores of 0.71, 0.73, and 0.58 across the three tasks, significantly outperforming single-server baselines. This work provides the first empirical validation of federated learning’s feasibility and effectiveness for decentralized content governance, establishing a scalable, privacy-preserving moderation paradigm for distributed communities operating without centralized oversight.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Multiagent Systems: Adversarial Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Content Management
• Resource Constraints
• Decentralized Environment

Innovation

• FedMod
• Federal Learning
• Content Management