BalancedSecAgg: Toward Fast Secure Aggregation for Federated Learning

Federated learning is a promising collaborative learning system from the perspective of training data privacy preservation; however, there is a risk of privacy leakage from individual local models of users. Secure aggregation protocols based on local model masking are a promising solution to prevent...

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Bibliographic Details
Main Authors: Hiroki Masuda, Kentaro Kita, Yuki Koizumi, Junji Takemasa, Toru Hasegawa
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10744018/
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