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- W3212887469 abstract "In order to increase the number of users involved in federated learning (FL) and further to reduce the training loss, a joint user-centric (U C) cluster formation and user selection problem in visible-light networks is studied in this paper. The original problem is non-convex and a heuristic distance-based algorithm is proposed by taking the training data size of each user into account, while satisfying the delay requirement. Based on a handwritten digit identification task, our simulation results demonstrate that the UC scheme is superior than the traditional network-centric (NC) design in terms of the total number of users participating in FL as well as the identification accuracy in all scenarios considered." @default.
- W3212887469 created "2021-11-22" @default.
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- W3212887469 date "2021-07-28" @default.
- W3212887469 modified "2023-09-22" @default.
- W3212887469 title "User-centric User Selection for Federated Learning in Visible-Light Networks" @default.
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- W3212887469 doi "https://doi.org/10.1109/iccc52777.2021.9580327" @default.
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