UniFL: Accelerating Federated Learning Using Heterogeneous Hardware Under a Unified Framework

UniFL: Accelerating Federated Learning Using Heterogeneous Hardware Under a Unified Framework

Federated learning (FL) is now considered a critical method for breaking down data silos. However, data encryption can significantly increase computing time, limiting its large-scale deployment. While hardware acceleration can be an effective solution, existing research has largely focused on a sing...

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Bibliographic Details
Main Authors: Biyao Che, Zixiao Wang, Ying Chen, Liang Guo, Yuan Liu, Yuan Tian, Jizhuang Zhao
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Federated learning
hardware acceleration
homomorphic encryption
privacy preserving
Online Access:https://ieeexplore.ieee.org/document/10374366/
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