Research on 5G Core Network Trust Model Based on NF Interaction Behavior

doi:10.3837/tiis.2022.10.007

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…3 is given as a FLR network topology representation. The employed topology is primarily based on Mininet software-defined networking (SDN) emulator and RYU controller (using Python-based custom scripts) [18][19][20][21][22], utilizing a testbed that supports programmable networking as follows:…”

Section: Model Evaluation and Deploymentmentioning

confidence: 99%

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Kang,

Ros,

Song

et al. 2023

CMC

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Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, softwaredefined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks. To establish the FLR framework for mission-critical healthcare applications, we provide a comprehensive workflow in this paper, introducing framework setup, iterative round communications, and model evaluation/deployment. Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization, which concludes with the generation of service experience batches for model deployment. To assess the practicality of our approach, we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset (Version 2.0.1) of the Korea Medical Panel Survey. Performance metrics, including end-to-end execution delays, model drop/delivery ratios, and final model accuracies, are captured and compared between the proposed FLR framework and other baseline schemes. Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks, addressing the critical demands of QoS reliability and privacy preservation.

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Section: Model Evaluation and Deploymentmentioning

confidence: 99%

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Kang,

Ros,

Song

et al. 2023

CMC

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A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking

Ros,

Tam,

Song

et al. 2024

era

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<abstract> <p>Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field.</p> </abstract>

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Research on 5G Core Network Trust Model Based on NF Interaction Behavior

Cited by 2 publications

References 15 publications

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

Real-Time Prediction Algorithm for Intelligent Edge Networks with Federated Learning-Based Modeling

A survey on state-of-the-art experimental simulations for privacy-preserving federated learning in intelligent networking

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