A Cross-Layer FL-Based Clustering Protocol to Support Multicast Routing in IoT-Enabled MANETs With CF-mMIMO by Amalia Amalia, Yushintia Pramitarini, Ridho Hendra Yoga Perdana, Kyusung Shim, Beongku An

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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

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SAFE-CAST: secure AI-federated enumeration for clustering-based automated surveillance and trust in machine-to-machine communication by Yusuf Kursat Tuncel, Kasım Öztoprak

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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

Subjects: “…Federal learning…”
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GenFedRL: a general federated reinforcement learning framework for deep reinforcement learning agents by Biao JIN, Yikang LI, Zhiqiang YAO, Yulin CHEN, Jinbo XIONG

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Pilot spoofing detection algorithm for edge nodes based on heterogeneous pilot energy estimation by Shiguo WANG, Shujuan TIAN, Qingyong DENG

“…For the federated learning scenarios with edge-end cooperation, edge servers and device terminals update their models and exchange gradient parameters frequently, and hence eavesdroppers can manipulate channel estimation through pilot spoofing to intercept the transmitted information and reduce the update efficiency of federated learning model.Therefore, a pilot attack detection algorithm with heterogeneous pilot energy estimation was proposed.Firstly, a federated learning pilot attack system model was constructed after the security of pilot attacks on data transmission had been analyzed.Then, a pilot attack detection method based on random segmentation and encryption methods was proposed to detect the pilot spoofing accurately and the contaminated channel could be recovered as well.Experimental results show that the proposed algorithm is more suitable for detecting pilot attacks with low transmit power and strong concealment compared to other existing algorithms.Furthermore, the data transmission rate of edge servers is improved significantly through the precoding based on the recovered channel state information.…”
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A Clonal Selection Optimization System for Multiparty Secure Computing by Minyu Shi, Yongting Zhang, Huanhuan Wang, Junfeng Hu, Xiang Wu

“…First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. …”
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A Secure Object Detection Technique for Intelligent Transportation Systems by Jueal Mia, M. Hadi Amini

“…Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. …”
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Distributed training of foundation models for ophthalmic diagnosis by Sina Gholami, Fatema-E Jannat, Atalie Carina Thompson, Sally Shin Yee Ong, Jennifer I. Lim, Theodore Leng, Hamed Tabkhivayghan, Minhaj Nur Alam

“…Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.…”
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Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals by Tsung-Ting Kuo, Rodney A. Gabriel, Jejo Koola, Robert T. Schooley, Lucila Ohno-Machado

“…We compared D-CLEF with centralized/siloed/federated learning in horizontal or vertical scenarios. …”
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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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Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood by Guan Wang, Zejun Sun, Tianqin Wang, Yuanzhe Li, Haifeng Hu

“…Abstract As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. …”
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Heterogeneous federated bidirectional knowledge distillation transfer semi-supervised modulation recognition by Peihan QI, Yuanlei DING, Kai YIN, Jiabo XU, Zan LI

“…The large-scale deployment and rapid development of the new generation mobile communication system underpin the widespread application of a massive and diverse range of Internet of things (IoT) devices.However, the distributed application of IoT devices results to significant disparities in private data and substantial heterogeneity in local processing models, which severely limits the aggregation capability of global intelligent model.Therefore, to tackle the challenges of data heterogeneity, model heterogeneity, and insufficient labeling faced by intelligent modulation recognition in cognitive IoT, an algorithm was proposed for heterogeneous federated bidirectional semi-supervised modulation recognition, which incorporated bidirectional knowledge distillation.In the proposed algorithm, a public pseudo dataset was generated by variational autoencoder in the cloud for supporting uplink global knowledge distillation, and adaptively sharing to the local devices for downlink heterogeneous knowledge distillation, while integrating a semi-supervised algorithm within the distillation process.The simulation results indicate that the proposed algorithm outperforms current federated learning algorithms in terms of effectiveness and applicability in the field of communication signal processing.…”
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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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Dispatching and control information freshness guaranteed resource optimization in simplified power Internet of things by Haijun LIAO, Zehan JIA, Zhenyu ZHOU, Nian LIU, Fei WANG, Zhong GAN, Xianjiong YAO

“…Information freshness conducts an important impact on the training accuracy of the distributed energy dispatching and control model.Poor dispatching and control information freshness will increase the loss function of the training model, reduce the reliability and economy of dispatching and control, and effect the real-time balance of energy supply and demand.Simplified power Internet of things can provide plug-and-play and multi-mode fusion communication support for distributed energy dispatching and control, but it still faces challenges of the inadaptability between cross-domain resource optimization and model training, and the difficulty in guaranteeing dispatching and control information freshness.To solve the above challenges, an information freshness aware-based communication-and-computation collaborative optimization algorithm (IFAC³O) was proposed, and the information freshness deviation was regulated by the awareness of deficit virtual queue evolution.On this basis, IFAC³O leveraged deep Q network and dispatching and control information freshness awareness to learn the channel allocation and batch size joint optimization strategy, thereby minimizing model loss function while guaranteeing long-term dispatching and control information freshness constraints.Compared with the federated DRL based low-latency resource allocation algorithm and adaptive federated learning-based batch size optimization algorithm, IFAC³O can reduce global loss function by 63.29% and 38.88% as well as improve information freshness by 20.59% and 57.69%.…”
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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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