The proliferation of mobile social networks (MSNs) has transformed information dissemination, leading to increased reliance on these platforms for news consumption. However, this shift has been accompanied by the widespread propagation of fake news, posing significant challenges in terms of public panic, political influence, and the obscuring of truth. Traditional data processing pipelines for fake news detection in MSNs suffer from lengthy response times and poor scalability, failing to address the unique characteristics of news in MSNs, such as prompt propagation, large-scale quantity, and rapid evolution. This paper introduces a novel system named Decaffe - a DHT Tree-Based Online Federated Fake News Detection system. Decaffe leverages distributed hash table (DHT)-based aggregation trees for scalability and real-time detection, and it employs two model fine-tuning methods for adapting to mobile network dynamics. The system's structure includes a root, branches, and leaves for effective dissemination of a pre-trained model and ensemble-based aggregation of predictive results. Decaffe uniquely combines centralized server-based and decentralized serverless model fine-tuning approaches with personalized model fine-tuning, addressing the challenges of real-time detection, scalability, and adaptability in the dynamic environment of MSNs.

翻译：移动社交网络的普及改变了信息传播方式，导致人们越来越依赖这些平台获取新闻。然而，这一转变伴随着假新闻的广泛传播，在公众恐慌、政治影响和真相遮蔽方面带来了重大挑战。传统面向移动社交网络中假新闻检测的数据处理管道存在响应时间长、可扩展性差等问题，无法应对移动社交网络中新闻的即时传播、大规模数量和快速演变等独特特征。本文提出了一种名为Decaffe的新型系统——基于DHT树的在线联邦式假新闻检测系统。Decaffe利用基于分布式哈希表的聚合树实现可扩展性与实时检测，并采用两种模型微调方法以适应移动网络动态特性。该系统结构包含根节点、分支节点和叶子节点，可有效分发预训练模型并实现基于集成的预测结果聚合。Decaffe创新性地将基于中心化服务器与去中心化无服务器的模型微调方法相结合，同时引入个性化模型微调，从而解决了移动社交网络动态环境中实时检测、可扩展性与适应性的挑战。