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APCSMA: Adaptive Personalized Client-Selection and Model-Aggregation Algorithm for Federated Learning in Edge Computing Scenarios [PDF]
EntropyWith the rapid advancement of the Internet and big data technologies, traditional centralized machine learning methods are challenged when dealing with large-scale datasets.
Xueting Ma +3 more
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Precision aggregated local models [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractLarge‐scale Gaussian process (GP) regression is infeasible for large training data due to cubic scaling of flops and quadratic storage involved in working with covariance matrices. Remedies in recent literature focus on divide‐and‐conquer, for example, partitioning into subproblems and inducing functional (and thus computational) independence ...
Adam M. Edwards, Robert B. Gramacy
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FedUA: An Uncertainty-Aware Distillation-Based Federated Learning Scheme for Image Classification
Information, 2023 Recently, federated learning (FL) has gradually become an important research topic in machine learning and information theory. FL emphasizes that clients jointly engage in solving learning tasks.
Shao-Ming Lee, Ja-Ling Wu
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IEEE Access, 2022 The smart healthcare system has improved the patients quality of life (QoL), where the records are being analyzed remotely by distributed stakeholders. It requires a voluminous exchange of data for disease prediction via the open communication channel, i.
Vishwa Amitkumar Patel +6 more
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Future Internet, 2023 Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the ...
Liangkun Yu +3 more
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Local Differential Privacy-Based Federated Learning under Personalized Settings
Applied Sciences, 2023 Federated learning is a distributed machine learning paradigm, which utilizes multiple clients’ data to train a model. Although federated learning does not require clients to disclose their original data, studies have shown that attackers can infer ...
Xia Wu, Lei Xu, Liehuang Zhu
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Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach
IEEE Access, 2020 Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then jointly aggregate a global DNN model at the central server ...
Dongdong Ye, Rong Yu, Miao Pan, Zhu Han
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Bootstrap aggregation for model selection in the model-free formalism [PDF]
Magnetic Resonance, 2021 The ability to make robust inferences about the dynamics of biological macromolecules using NMR spectroscopy depends heavily on the application of appropriate theoretical models for nuclear spin relaxation.
T. Crawley, A. G. Palmer III
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Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism
IEEE Access, 2020 Short-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task.
Pan Zeng, Min Jin, Md. Fazla Elahe
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Shanghai Jiaotong Daxue xuebao, 2021 Aimed at the problems of wide area distribution, resource dispersion, and inefficient aggregation of distributed energy storage, this paper proposes an aggregation model and evaluation method of distributed energy storage based on the adaptive ...
YE Peng, LIU Siqi, GUAN Duojiao, JIANG Zhunan, SUN Feng, GU Haifei
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