Federated Learning With Privacy-Preserving Ensemble Attention Distillation

Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facil...

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Veröffentlicht in:IEEE transactions on medical imaging Jg. 42; H. 7; S. 2057 - 2067
Hauptverfasser: Gong, Xuan, Song, Liangchen, Vedula, Rishi, Sharma, Abhishek, Zheng, Meng, Planche, Benjamin, Innanje, Arun, Chen, Terrence, Yuan, Junsong, Doermann, David, Wu, Ziyan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Data models
Distillation
Federated learning
Hospitals
Humans
Image classification
Image processing
Image reconstruction
Image segmentation
Leakage
Machine Learning
Privacy
Risk reduction
Servers
Task analysis
Training
ISSN:0278-0062, 1558-254X, 1558-254X
Online-Zugang:Volltext
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Zusammenfassung:Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. This is particularly relevant for clinical applications since patient data are usually not allowed to be transferred out of medical facilities, leading to the need for FL. Existing FL methods typically share model parameters or employ co-distillation to address the issue of unbalanced data distribution. However, they also require numerous rounds of synchronized communication and, more importantly, suffer from a privacy leakage risk. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work. The central model is learned from local knowledge via ensemble attention distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage. We demonstrate that our method achieves very competitive performance with more robust privacy preservation based on extensive experiments on image classification, segmentation, and reconstruction tasks.
Bibliographie:ObjectType-Article-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2022.3213244