Energy-Efficient Link Selection for Decentralized Learning via Smart Devices with Edge Computing

Ching, Cheng-Wei; Yang, Chung-Kai; Liu, Kao–Lang; Hsu, Chia‐Wei; Kuo, Jian-Jhih; Huang, Hung-Sheng; Lee, Jen-Feng

doi:10.1109/globecom42002.2020.9322306

Cited by 3 publications

(1 citation statement)

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“…To mitigate the possible communication and computational bottleneck, we device the decentralized serverless model fine-tuning. Specifically, the leaf nodes first perform local model fine-tuning and then upload their gradients to one of leaf nodes that have social links one another [13,17]. After receiving the intermediate fine-tuned models from their trust leaf nodes, the leaf nodes aggregate the intermediate fine-tuned models and continue uploading gradients until the root receives the intermediate fine-tuned models.…”

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

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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.

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

confidence: 99%