Efficient Communication Topology via Partially Differential Privacy for Decentralized Learning

Ching, Cheng-Wei; Huang, Hung-Sheng; Yang, Chun-An; Liu, Kao–Lang; Kuo, Jian-Jhih

doi:10.1109/icccn52240.2021.9522327

Cited by 4 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In spite of the simplicity of the centralized server-based model fine-tuning, the entire aggregation tree easily suffer from communication and computational bottlenecks that happen to each member node [24], especially in a mobile social network, where the large-scale quantity of news and the rapid quantity of data are two critical characteristics of MSNs. Moreover, to secure the user data from the model inversion attacks that reproduce the data for model fine-tuning [12,14,15], some security-intensive applications incorporate random noises into model fine-tuning [6,12]. Such a random noise is proved to pose negative impact on predictive performance.…”

Section: Online Model Fine-tuning and Ensemble-based Model Inferencementioning

confidence: 99%

Decaffe: DHT Tree-Based Online Federated Fake News Detection

Ching,

2024

2024 8th International Conference on Control Engineering and Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

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 ensemblebased aggregation of predictive results. Decaffe uniquely combines centralized server-based and decentralized serverless model finetuning approaches with personalized model fine-tuning, addressing the challenges of real-time detection, scalability, and adaptability in the dynamic environment of MSNs. CCS Concepts• Computer systems organization → Peer-to-peer architectures.

show abstract