Energy-Efficient Topology Construction via Power Allocation for Decentralized Learning via Smart Devices With Edge Computing

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

doi:10.1109/tgcn.2021.3096884

Cited by 11 publications

(4 citation statements)

References 44 publications

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“…We can observe that an one-trip communication from leaf nodes to the root and back to leaf nodes is necessary for the centralized server-based model fine-tuning. 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].…”

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%

Decaffe: DHT Tree-Based Online Federated Fake News Detection

Ching,

2024

2024 8th International Conference on Control Engineering and Artificial Intelligence

Self Cite

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“…Aggregating: The most common aggregation method is majority voting for classification tasks (selecting the class with the most votes). [16] The base model of random forest is decision tree, thus, when build the model, we need to consider hyperparemeters like splitting criteria, minimum samples in leaf, etc. into account.…”

Section: Random Forestmentioning

confidence: 99%

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Li,

Zhu,

Liu

et al. 2024

IJCSIT

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This study conducts a thorough examination of malware detection using machine learning techniques, focusing on the evaluation of various classification models using the Mal-API-2019 dataset. The aim is to advance cybersecurity capabilities by identifying and mitigating threats more effectively. Both ensemble and non-ensemble machine learning methods, such as Random Forest, XGBoost, K Nearest Neighbor (KNN), and Neural Networks, are explored. Special emphasis is placed on the importance of data pre-processing techniques, particularly TF-IDF representation and Principal Component Analysis, in improving model performance. Results indicate that ensemble methods, particularly Random Forest and XGBoost, exhibit superior accuracy, precision, and recall compared to others, highlighting their effectiveness in malware detection. The paper also discusses limitations and potential future directions, emphasizing the need for continuous adaptation to address the evolving nature of malware. This research contributes to ongoing discussions in cybersecurity and provides practical insights for developing more robust malware detection systems in the digital era.

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“…Although context-agnostic methods can always be applied to improve the energy efficiency and the utilization rate of resources, an essential and unique feature of decentralized learning is the possibility of adjusting the communication network topology. In [29], the authors select a subset of the available links to perform model broadcasting so as to minimize the transmission power levels. A constraint on the minimum number of links required to guarantee the convergence of learning is added.…”

Section: Context-aware DL Optimizationmentioning

confidence: 99%

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

Shalavi¹,

Perin²,

Zanella³

et al. 2022

Preprint

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Mobile networks are becoming energy hungry, and this trend is expected to continue due to a surge in communication and computation demand. Multi-access Edge Computing (MEC), a key component of 5𝐺 and 6𝐺 networks, will entail energy-consuming services and applications, with non-negligible impact in terms of ecological sustainability. In this paper, we provide a comprehensive review of existing approaches to make edge computing networks greener, including but not limited to the exploitation of renewable energy resources, and context-awareness (e.g., user mobility, or dynamics in the traffic), analyzing their pros and cons. We hence provide an updated account of recent developments on MEC from an energetic sustainability perspective, addressing the initial deployment of computing resources, their dynamic (re)allocation (resource scheduling), as well as distributed and federated learning designs. In doing so, we highlight the energy aspects of these algorithms, advocating the need for energy-sustainable edge computing systems that are aligned with Sustainable Development Goals (SDGs) and the Paris agreement. To the best of our knowledge, this is the first work providing a systematic literature review on the efficient deployment and management of energy harvesting MEC, with special focus on the deployment, provisioning, and scheduling of computing tasks, including federated learning for distributed edge intelligence, toward making edge networks greener and more sustainable. At the end of the paper, open research avenues and challenges are identified for all the surveyed topics. CCS Concepts: • Networks → Network performance evaluation; Wireless access points, base stations and infrastructure; • Computer systems organization → Embedded systems; • Computing methodologies → Planning and scheduling.

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Energy-Efficient Topology Construction via Power Allocation for Decentralized Learning via Smart Devices With Edge Computing

Cited by 11 publications

References 44 publications

Decaffe: DHT Tree-Based Online Federated Fake News Detection

Decaffe: DHT Tree-Based Online Federated Fake News Detection

Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Energy Efficient Deployment and Orchestration of Computing Resources at the Network Edge: a Survey on Algorithms, Trends and Open Challenges

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