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- W4312569595 abstract "This paper proposes FedDFA (Federated Dual-Factor Aggregation), an effective aggregation strategy for the application of federated learning in the field of vision-based driver distraction detection task. We argue that simply treating this task as an image recognition task is not enough. Image characteristics are further explored in this paper and we found that the driver’s diversity is an important factor. Based on the image characteristic analysis, FedDFA is introduced, which calculates the aggregation weights based on the number of images and that of drivers on each client for better parameter aggregation during federated learning. Extensive experiments are conducted and experimental results show that FedDFA achieves satisfactory performance." @default.
- W4312569595 created "2023-01-05" @default.
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- W4312569595 date "2022-01-01" @default.
- W4312569595 modified "2023-10-18" @default.
- W4312569595 title "FedDFA: Dual-Factor Aggregation for Federated Driver Distraction Detection" @default.
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- W4312569595 doi "https://doi.org/10.1007/978-981-19-7532-5_15" @default.
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