Matches in SemOpenAlex for { <https://semopenalex.org/work/W3200307514> ?p ?o ?g. }
Showing items 1 to 84 of
84
with 100 items per page.
- W3200307514 abstract "Federated learning is a distributed deep learning method that enables parallel and distributed learning of data on multiple participants, without the need to centrally store it. In intelligent transportation system, it is impractical to gather the vehicle data from the edge devices due to data privacy concerns or network bandwidth limitation. Hence, combining with federated learning to train vehicle data processing models has become one of the popular solutions. However, such computing paradigm is subject to threats posed by malicious and abnormal nodes that greatly reduces the computing power of the neural network when performing calculations in a distributed manner. In this paper, we use the Vehicle Energy Dataset to simulate distributed vehicle data. Based on these data, we propose an unsupervised anomaly removal and neural network model based on federated learning to solve the problem of outlier data on vehicle equipment and analyze the effect of speed on fuel consumption. The results show that with the proposed anomaly removal strategy, MAE and MSE of the trained network are 29% and 36% lower than those without anomaly removal, respectively." @default.
- W3200307514 created "2021-09-27" @default.
- W3200307514 creator A5014235816 @default.
- W3200307514 creator A5016038454 @default.
- W3200307514 creator A5052848122 @default.
- W3200307514 creator A5067290556 @default.
- W3200307514 date "2021-07-18" @default.
- W3200307514 modified "2023-09-25" @default.
- W3200307514 title "Anomaly Removal for Vehicle Energy Consumption in Federated Learning" @default.
- W3200307514 cites W2000661457 @default.
- W3200307514 cites W2122538988 @default.
- W3200307514 cites W2166448956 @default.
- W3200307514 cites W2551621012 @default.
- W3200307514 cites W2589456662 @default.
- W3200307514 cites W2768181417 @default.
- W3200307514 cites W2790864385 @default.
- W3200307514 cites W2796882046 @default.
- W3200307514 cites W2905737086 @default.
- W3200307514 cites W2928675906 @default.
- W3200307514 cites W2932010921 @default.
- W3200307514 cites W2966559104 @default.
- W3200307514 cites W3001299093 @default.
- W3200307514 cites W3010852232 @default.
- W3200307514 cites W3041934925 @default.
- W3200307514 cites W3042621011 @default.
- W3200307514 doi "https://doi.org/10.1109/ijcnn52387.2021.9533419" @default.
- W3200307514 hasPublicationYear "2021" @default.
- W3200307514 type Work @default.
- W3200307514 sameAs 3200307514 @default.
- W3200307514 citedByCount "1" @default.
- W3200307514 countsByYear W32003075142023 @default.
- W3200307514 crossrefType "proceedings-article" @default.
- W3200307514 hasAuthorship W3200307514A5014235816 @default.
- W3200307514 hasAuthorship W3200307514A5016038454 @default.
- W3200307514 hasAuthorship W3200307514A5052848122 @default.
- W3200307514 hasAuthorship W3200307514A5067290556 @default.
- W3200307514 hasConcept C108583219 @default.
- W3200307514 hasConcept C119599485 @default.
- W3200307514 hasConcept C119857082 @default.
- W3200307514 hasConcept C120314980 @default.
- W3200307514 hasConcept C124101348 @default.
- W3200307514 hasConcept C127413603 @default.
- W3200307514 hasConcept C154945302 @default.
- W3200307514 hasConcept C2780165032 @default.
- W3200307514 hasConcept C41008148 @default.
- W3200307514 hasConcept C50644808 @default.
- W3200307514 hasConcept C67186912 @default.
- W3200307514 hasConcept C739882 @default.
- W3200307514 hasConcept C77088390 @default.
- W3200307514 hasConcept C79337645 @default.
- W3200307514 hasConcept C79403827 @default.
- W3200307514 hasConceptScore W3200307514C108583219 @default.
- W3200307514 hasConceptScore W3200307514C119599485 @default.
- W3200307514 hasConceptScore W3200307514C119857082 @default.
- W3200307514 hasConceptScore W3200307514C120314980 @default.
- W3200307514 hasConceptScore W3200307514C124101348 @default.
- W3200307514 hasConceptScore W3200307514C127413603 @default.
- W3200307514 hasConceptScore W3200307514C154945302 @default.
- W3200307514 hasConceptScore W3200307514C2780165032 @default.
- W3200307514 hasConceptScore W3200307514C41008148 @default.
- W3200307514 hasConceptScore W3200307514C50644808 @default.
- W3200307514 hasConceptScore W3200307514C67186912 @default.
- W3200307514 hasConceptScore W3200307514C739882 @default.
- W3200307514 hasConceptScore W3200307514C77088390 @default.
- W3200307514 hasConceptScore W3200307514C79337645 @default.
- W3200307514 hasConceptScore W3200307514C79403827 @default.
- W3200307514 hasFunder F4320335777 @default.
- W3200307514 hasLocation W32003075141 @default.
- W3200307514 hasOpenAccess W3200307514 @default.
- W3200307514 hasPrimaryLocation W32003075141 @default.
- W3200307514 hasRelatedWork W1972866788 @default.
- W3200307514 hasRelatedWork W2010489518 @default.
- W3200307514 hasRelatedWork W2406743792 @default.
- W3200307514 hasRelatedWork W2770458211 @default.
- W3200307514 hasRelatedWork W3016613306 @default.
- W3200307514 hasRelatedWork W4223943233 @default.
- W3200307514 hasRelatedWork W4312200629 @default.
- W3200307514 hasRelatedWork W4312874833 @default.
- W3200307514 hasRelatedWork W4380075502 @default.
- W3200307514 hasRelatedWork W961884 @default.
- W3200307514 isParatext "false" @default.
- W3200307514 isRetracted "false" @default.
- W3200307514 magId "3200307514" @default.
- W3200307514 workType "article" @default.