Probabilistic forecasting of multiple plant day-ahead renewable power generation sequences with data privacy preserving by Hong Liu, Zijun Zhang

“…To realize such a task, an advanced domain-invariant feature learning embedded federated learning (DIFL) framework is proposed to coordinate the development of a system of deep network-based models serving as multiple clients and one server. …”
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Multi-key fully homomorphic encryption scheme based on NTRU bootstrapping by ZHENG Junhua, JIANG Hongwei, LIU Rong, LI Yixiu, LI Wen, WENG Jian

“…Multi-key fully homomorphic encryption (MK-FHE) technology supports homomorphic operations on ciphertexts encrypted with different keys, and can be directly applied to real-world multi-user data fusion computing scenarios, such as multi-party collaborative computing and federated learning. At present, mainstream multi-key fully homomorphic encryption mainly uses bootstrapping technology to achieve multi-key homomorphic computing of LWE (learning with errors) encrypted ciphertexts. …”
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SecEdge: A novel deep learning framework for real-time cybersecurity in mobile IoT environments by Kamran Ahmad Awan, Ikram Ud Din, Ahmad Almogren, Ali Nawaz, Muhammad Yasar Khan, Ayman Altameem

“…The SecEdge framework integrates transformer-based models for efficient handling of long-range dependencies and Graph Neural Networks (GNNs) for modeling relational data, coupled with federated learning to ensure data privacy and reduce latency. …”
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A Modular AI-Driven Intrusion Detection System for Network Traffic Monitoring in Industry 4.0, Using Nvidia Morpheus and Generative Adversarial Networks by Beatrice-Nicoleta Chiriac, Florin-Daniel Anton, Anca-Daniela Ioniță, Bogdan-Valentin Vasilică

“…The proposed IDS has a fast rate of analysis, managing more than 500,000 inputs in almost 10 s, due to the application of the federated learning methodology. The classification performance of the model was improved by integrating a generative adversarial network (GAN) that generates polymorphic network traffic packets.…”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). It explores AD approaches, datasets, technologies, complexities, and obstacles, emphasizing the requirement for effective detection across domains. …”
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Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety by Haohan Ding, Haoke Hou, Long Wang, Xiaohui Cui, Wei Yu, David I. Wilson

“…This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. …”
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Cybersecurity Solutions for Industrial Internet of Things–Edge Computing Integration: Challenges, Threats, and Future Directions by Tamara Zhukabayeva, Lazzat Zholshiyeva, Nurdaulet Karabayev, Shafiullah Khan, Noha Alnazzawi

“…The review emphasizes the integration of advanced security technologies, including machine learning (ML), federated learning (FL), blockchain, blockchain–ML, deep learning (DL), encryption, cryptography, IT/OT convergence, and digital twins, as essential for enhancing the security and real-time data protection of CPS in IIoT–edge computing. …”
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Predicting survival in malignant glioma using artificial intelligence by Wireko Andrew Awuah, Adam Ben-Jaafar, Subham Roy, Princess Afia Nkrumah-Boateng, Joecelyn Kirani Tan, Toufik Abdul-Rahman, Oday Atallah

“…Solutions such as federated learning, lightweight AI models and explainable AI frameworks are proposed to overcome these barriers. …”
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Review of privacy computing techniques for multi-party data fusion analysis by LIU Shenglong, HUANG Xiuli, JIANG Yiwen, JIANG Jiawei, TIAN Yuechi, ZHOU Zejun, NIU Ben

“…There were still limitations for anonymity, scrambling, or access control-based traditional privacy desensitization measures, cryptography-based measures, and federated learning-based measures, while privacy computing theory provided a computational and information system framework for full-lifecycle protection, which needed to be combined with different application scenarios to implement full-lifecycle privacy information protection.…”
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Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory by Naoki Masuyama, Yusuke Nojima, Yuichiro Toda, Chu Kiong Loo, Hisao Ishibuchi, Naoyuki Kubota

“…In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. …”
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USING ARTIFICIAL INTELLIGENCE (AI) AND DEEP LEARNING TECHNIQUES IN FINANCIAL RISK MANAGEMENT by Joseph Olorunfemi AKANDE

“…One notable area of recent development is the incorporation of uncertainty estimation techniques in machine learning models, which allow for more precise risk assessment in unpredictable financial environments. Moreover, federated learning systems present a promising solution for ensuring privacy and security when dealing with sensitive financial data. …”
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USING ARTIFICIAL INTELLIGENCE (AI) AND DEEP LEARNING TECHNIQUES IN FINANCIAL RISK MANAGEMENT by Joseph Olorunfemi AKANDE

“…One notable area of recent development is the incorporation of uncertainty estimation techniques in machine learning models, which allow for more precise risk assessment in unpredictable financial environments. Moreover, federated learning systems present a promising solution for ensuring privacy and security when dealing with sensitive financial data. …”
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Advancing privacy-aware machine learning on sensitive data via edge-based continual µ-training for personalized large models by Zhaojing Huang, Leping Yu, Luis Fernando Herbozo Contreras, Kamran Eshraghian, Nhan Duy Truong, Armin Nikpour, Omid Kavehei

“…Moreover, weight transfer in our system is exclusively for fine-tuning; hence, it improves user privacy protection by failing data reconstruction attempts from weights, like an issue with Federated learning models. Our on-device fine-tuning prevents the transferring of data or gradients from the edge of the network to their server. …”
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Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar, Abdullatif Albaseer, Muhammad Asif Khan, Mohamed Abdallah, Hamid Menouar, Saif Al-Kuwari, Ala Al-Fuqaha

“…Additionally, we explore emerging technologies, such as Federated Learning (FL) and Transfer Learning, to enhance the robustness and adaptability of IDS solutions. …”
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Physical unclonable in-memory computing for simultaneous protecting private data and deep learning models by Wenshuo Yue, Kai Wu, Zhiyuan Li, Juchen Zhou, Zeyu Wang, Teng Zhang, Yuxiang Yang, Lintao Ye, Yongqin Wu, Weihai Bu, Shaozhi Wang, Xiaodong He, Xiaobing Yan, Yaoyu Tao, Bonan Yan, Ru Huang, Yuchao Yang

“…It potentially serves as the hardware infrastructure for edge devices in federated learning or other systems.…”
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Explainable AI for Healthcare 5.0: Opportunities and Challenges by Deepti Saraswat, Pronaya Bhattacharya, Ashwin Verma, Vivek Kumar Prasad, Sudeep Tanwar, Gulshan Sharma, Pitshou N. Bokoro, Ravi Sharma

“…A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. …”
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Editorial by Teddy Surya Gunawan

“…Healthcare and safety remain pivotal in this issue, with studies delving into early autism screening using federated learning and diabetic retinopathy detection leveraging deep convolutional neural networks. …”
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New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies by Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU

“…Methods: Using the federated learning network architecture, this paper analyzes the resource allocation and user scheduling schemes: 1. …”
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Blockchain empowered 6G by Yueyue DAI, Ke ZHANG, Yan ZHANG

“…6G network not only explore higher communication rates but also combine with the emerging technologies such as cloud computing,edge computing,artificial intelligence,and big data,and use a new network architecture to achieve wider interconnection and interoperability across network and domains,and provide an intelligent,safe and efficient technical support for industrial Internet of things,smart cities,and intelligent transportation.Blockchain technology as a decentralized,open and transparent distributed ledger technology can provide strong security for 6G.The integration of blockchain technology and 6G network was studied.Firstly,the difference between 5G and 6G network and the challenges of 6G faced were given,and the research of blockchain technology in spectrum management,mobile edge computing and D2D communication was reviewed.Then,the integration of blockchain technology and 6G emerging network was explored.Thus,the technical challenges in terms of blockchain and the cloud-edge-device network,blockchain and federal learning,blockchain-based resource transactions,and lightweight blockchain for edge network were presented.The corresponding solutions were also given.…”
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Hypernetwork link prediction method based on the SCL-CMM model by REN Yuyuan, MA Hong, LIU Shuxin, WANG Kai

“…The original hypergraph was reconstructed into multiple homoprotonic graphs, and the distribution of potential hyperlinks was inferred within the observation space of the subgraph, rather than the entire adjacency space, in order to restore the complete hypernetwork structure. This method federated learned the structural characteristics and aggregation attributes of hypernetworks to model the high-order nonlinear behavior of each subgraph, thereby solving the problems of single category and low precision in heterogeneous hypergraphs link prediction. …”
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