Decaffe: DHT Tree-Based Online Federated Fake News Detection
CoRR(2023)
摘要
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.
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