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- W4327767982 abstract "Federated learning (FL) is a promising technique to collaboratively train a model with distributed users and datasets. To develop communication-efficient FL systems, model-size reduction by using a winning ticket of the lottery ticket hypothesis has been proposed and investigated; however, it is still an issue how to discover winning tickets in FL systems, which incur communication costs for model training to find the winning ticket. To address this issue, we propose a method of using a surrogate dataset that can be synthetically generated at an FL server, instead of the distributed target datasets found at the clients, for finding the winning ticket. The method is based on observations that winning tickets obtained from a large dataset could be transferable to other tasks. The performance evaluations using a subset of the FEMNIST (as target) and Chars 74K (as emulated surrogate) datasets show that the proposed method reduces communication cost in FL by about 80%." @default.
- W4327767982 created "2023-03-19" @default.
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- W4327767982 creator A5044259247 @default.
- W4327767982 date "2023-01-08" @default.
- W4327767982 modified "2023-09-27" @default.
- W4327767982 title "Compressing Model before Federated Learning by Transferrable Surrogate Lottery Ticket" @default.
- W4327767982 cites W2405578611 @default.
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- W4327767982 doi "https://doi.org/10.1109/ccnc51644.2023.10060578" @default.
- W4327767982 hasPublicationYear "2023" @default.
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