LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment

LAFD: Local-Differentially Private and Asynchronous Federated Learning With Direct Feedback Alignment

Federated learning is a promising approach for training machine learning models using distributed data from multiple mobile devices. However, privacy concerns arise when sensitive data are used for training. In this paper, we discuss the challenges of applying local differential privacy to federated...

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Bibliographic Details
Main Authors:	Kijung Jung, Incheol Baek, Soohyung Kim, Yon Dohn Chung
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Series:	IEEE Access
Subjects:	Direct feedback alignment federated learning local differential privacy privacy-preserving deep learning
Online Access:	https://ieeexplore.ieee.org/document/10216288/

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