DP-FedCMRS: Privacy-Preserving Federated Learning Algorithm to Solve Heterogeneous Data

DP-FedCMRS: Privacy-Preserving Federated Learning Algorithm to Solve Heterogeneous Data

In federated learning, non-independently and non-identically distributed heterogeneous data on the clients can limit both the convergence speed and model utility of federated learning, and gradients can be used to infer original data, posing a threat to user privacy. To address these issues, this pa...

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Bibliographic Details
Main Authors: Yang Zhang, Shigong Long, Guangyuan Liu, Junming Zhang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Federated learning
differential privacy
non-IID data
clustering
Online Access:https://ieeexplore.ieee.org/document/10910083/
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https://ieeexplore.ieee.org/document/10910083/

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