A privacy budget adaptive optimization scheme for federated computing power Internet of things by MA Wenyu, CHEN Qian, HU Yuxiang, YAN Haonan, HU Tao, YI Peng

“…Therefore, an adaptive optimization scheme for privacy budget was proposed in federated computing power IoT, which was called federated learning based on Cramér-Rao lower bound differential privacy (FedCDP). …”
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Advancements of Deep Learning Model-Based Rehabilitation Training System by Xu Chiyu

“…The experimental results show that there is still room for the development of rehabilitation training assessment systems in terms of privacy, interpretation ability, and application scenarios, and that researchers can address the above issues by using federated learning, developing an expert system, and using transfer learning domain adaptation, respectively.…”
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A Comprehensive Survey of Deep Learning Approaches in Image Processing by Maria Trigka, Elias Dritsas

“…Finally, this survey identifies potential future directions, including the integration of emerging technologies like quantum computing and neuromorphic architectures for enhanced efficiency and federated learning for privacy-preserving training. Additionally, it highlights the potential of combining DL with emerging technologies such as edge computing and explainable artificial intelligence (AI) to address scalability and interpretability challenges. …”
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Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization by Javier Garcia-Barcos, Ruben Martinez-Cantin

“…Fully distributed BO addresses the need for efficient parallel and asynchronous active search, especially where traditional centralized BO faces limitations concerning privacy in federated learning and resource utilization in high-performance computing settings. …”
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The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection by Tarek Berghout

“…Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. …”
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Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities by Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas, Adrianna Piszcz

“…Generative adversarial networks (GANs) are used to simulate energy usage scenarios, supporting strategic planning and anomaly detection. Federated learning ensures privacy-preserving data sharing in distributed energy systems, promoting collaboration without compromising security. …”
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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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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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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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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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