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- W4385329462 abstract "When using federated learning (FL) to predict user preferences, not all data samples are equally useful. For example, in video recommendation, recent watch history is more useful than older watch history. Thus, a new FL approach with adaptive weighted model aggregation (FLAMA) is proposed, where model aggregation weights are recalculated each FL training round based on the number of useful data samples used by each client and global model performance. Additionally, training datasets are compiled with a higher preference for useful data samples via prioritized data sample selection (PDSS). It is demonstrated that the proposed FLAMA achieves a higher accuracy in less FL training rounds compared to other schemes." @default.
- W4385329462 created "2023-07-29" @default.
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- W4385329462 date "2023-06-19" @default.
- W4385329462 modified "2023-09-24" @default.
- W4385329462 title "Federated Learning with Adaptive Weighted Model Aggregation" @default.
- W4385329462 doi "https://doi.org/10.1109/iwqos57198.2023.10188800" @default.
- W4385329462 hasPublicationYear "2023" @default.
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