Cloud federation three-layer architecture: game-theoretic QoS modeling by MA Kun, HU Chuangyue, ZHANG Yuzhi, MA Fangchao, WANG Xiaodong, DANG Zhonghua, XIAO Wenhong, CHEN Shuangxi

“…However, there is a complex interplay between pursuing individual maximum benefits and ensuring the overall quality of service (QoS) of the federation. A QoS based cloud federation model was proposed to address the above issues, covering a three-layer architecture of cloud computing. …”
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Double layer federated security learning architecture for artificial intelligence of things by ZHENG Chengbo, YAN Haonan, FU Caili, ZHANG Dong, LI Hui, WANG Bin

“…Federated learning, as a distributed machine learning architecture, can complete model co-training while protecting data privacy, and is widely used in Artificial Intelligence of Things. …”
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Decentralizing security architecture: evaluating federalism’s role in enhancing security frameworks in Nigeria by O. O. Awotayo, B. O. Omitola

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Space-Air-Ground Integrated Network Architecture Based on Mobile Edge Computing by Jian WU, Min JIA, Qing GUO

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Building an African Counter-Terrorism Architecture by Jolaade Omede, Arinze Ngwube

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Review of communication optimization methods in federated learning by YANG Zhikai, LIU Yaping, ZHANG Shuo, SUN Zhe, YAN Dingyu

“…Then, based on the factors affecting communication efficiency, existing federated learning communication optimization methods were comprehensively sorted out and analyzed from optimization objectives such as model parameter compression, model update strategies, system architecture, and communication protocols. …”
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On-Site Participation for Proto-Architectural Assemblies Encompassing Technology and Human Improvisation: “Fish Trap” and “Orchid” Architectural Interventions by Peter Buš, Shi-Yen Wu, Ayça Tartar

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Federated learning assisted distributed energy optimization by Yuhan Du, Nuno Mendes, Simin Rasouli, Javad Mohammadi, Pedro Moura

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SCIENTIFIC AND CREATIVE PORTRAIT OF THE ACADEMY OF ARCHITECTURE AND ARTS by Alexandr M. Boyarinov, Viktoriya V. Pischulina

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A survey of federated learning for 6G networks by Guanglei GENG, Bo GAO, Ke XIONG, Pingyi FAN, Yang LU, Yuwei WANG

“…It is an important feature of the 6G that how to realize everything interconnection through large-scale complex heterogeneous networks based on native artificial intelligence (AI).Thanks to the distinct machine learning architecture of data processing locally, federated learning (FL) is regarded as one of the promising solutions to incorporate distributed AI in 6G scenarios, and has become a critical research direction of 6G.Therefore, the necessity of introducing distributed AI into the future 6G especially for internet of things (IoT) scenarios was analyzed.And then, the potentials of FL in meeting the 6G requirements were discussed, and the state-of-the-arts of FL related technologies such as architecture design, resource utilization, data transmission, privacy protection, and service provided for 6G were investigated.Finally, several key technical challenges and potential valuable research directions for FL-empowered 6G were put forward.…”
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Adversarial sample generation algorithm for vertical federated learning by Xiaolin CHEN, Daoguang ZAN, Bingchao WU, Bei GUAN, Yongji WANG

“…To adapt to the scenario characteristics of vertical federated learning (VFL) applications regarding high communication cost, fast model iteration, and decentralized data storage, a generalized adversarial sample generation algorithm named VFL-GASG was proposed.Specifically, an adversarial sample generation framework was constructed for the VFL architecture.A white-box adversarial attack in the VFL was implemented by extending the centralized machine learning adversarial sample generation algorithm with different policies such as L-BFGS, FGSM, and C＆amp;W.By introducing deep convolutional generative adversarial network (DCGAN), an adversarial sample generation algorithm named VFL-GASG was designed to address the problem of universality in the generation of adversarial perturbations.Hidden layer vectors were utilized as local prior knowledge to train the adversarial perturbation generation model, and through a series of convolution-deconvolution network layers, finely crafted adversarial perturbations were produced.Experiments show that VFL-GASG can maintain a high attack success while achieving a higher generation efficiency, robustness, and generalization ability than the baseline algorithm, and further verify the impact of relevant settings for adversarial attacks.…”
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Data integrity verification based on model cloud federation of TPA by Junfeng TIAN, Tianle LI

“…Aiming at the untrustworthiness of third-party auditor (TPA) in the publicity verification model,a data integrity verification model based on the cloud federation of TPA was proposed.Firstly,the cloud federation of TPA’s architecture was designed and the main functional components and function of the system platform was defined.The federation could manage and control the TPA cloud members.Secondly,TPA was designed in detail by using trusted computing technology and blockchain technology to ensure the credibility of the TPA execution environment and workflow.Finally,the data integrity verification model was built by using cloud federation of TPA.The correctness,security and effectiveness of the model were analyzed theoretically and experimentally.…”
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LSKE: Lightweight Secure Key Exchange Scheme in Fog Federation by Yashar Salami, Vahid Khajehvand

“…The fog computing architecture allows data exchange with the vehicle network, sensor networks, etc. …”
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Federated Learning: A Distributed Shared Machine Learning Method by Kai Hu, Yaogen Li, Min Xia, Jiasheng Wu, Meixia Lu, Shuai Zhang, Liguo Weng

“…Federated learning (FL) is a distributed machine learning (ML) framework. …”
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Distribution of transaction costs in the investment and construction sector of the Russian Federation by R. R. Kozakov, V. A. Koshcheev

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Evaluation and optimization of carbon emission for federal edge intelligence network by Peng ZHANG, Yong XIAO, Jiwei HU, Liang LIAO, Jianxin LYU, Zegang BAI

“…In recent years, the continuous evolution of communication technology has led to a significant increase in energy consumption.With the widespread application and deep deployment of artificial intelligence (AI) technology and algorithms in telecommunication networks, the network architecture and technological evolution of network intelligent will pose even more severe challenges to the energy efficiency and emission reduction of future 6G.Federated edge intelligence (FEI), based on edge computing and distributed federated machine learning, has been widely acknowledged as one of the key pathway for implementing network native intelligence.However, evaluating and optimizing the comprehensive carbon emissions of federated edge intelligence networks remains a significant challenge.To address this issue, a framework and a method for assessing the carbon emissions of federated edge intelligence networks were proposed.Subsequently, three carbon emission optimization schemes for FEI networks were presented, including dynamic energy trading (DET), dynamic task allocation (DTA), and dynamic energy trading and task allocation (DETA).Finally, by utilizing a simulation network built on real hardware and employing real-world carbon intensity datasets, FEI networks lifecycle carbon emission experiments were conducted.The experimental results demonstrate that all three optimization schemes significantly reduce the carbon emissions of FEI networks under different scenarios and constraints.This provides a basis for the sustainable development of next-generation intelligent communication networks and the realization of low-carbon 6G networks.…”
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Evaluating Enterprise Architecture Frameworks for Digital Transformation in Agriculture by Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah, Sabareswari Thiyagarajan, Baarathi Balasubramaniam, Yunus Bin Yousif, Suraya Mohammad, Khairul Shafee Kalid

“…The objective is to evaluate and compare four leading EA frameworks—the Zachman Framework (ZFEA), the Department of Defense Architecture Framework (DoDAF), the Federal Enterprise Architecture Framework (FEAF), and The Open Group Architecture Framework (TOGAF)—using eight key criteria: adaptability, scalability, ease of implementation, support for modern technologies, cost-effectiveness, compliance, traceability, and risk management. …”
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La tour-porte du château de Marmande à Vellèches (Vienne) du xiie au xvie s. : une chronologie renouvelée by Julien Noblet

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Privacy-enhanced federated learning scheme based on generative adversarial networks by Feng YU, Qingxin LIN, Hui LIN, Xiaoding WANG

“…Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed.…”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…This paper presents a detailed literature review of the application of Federated Learning (FL) in brain tumor classification, focusing on the necessity of privacy-preserving ML using Magnetic Resonance Imaging (MRI) technology. …”
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