Robust aggregation for adaptive privacy preserving federated learning in
healthcare
healthcare
File(s)
Working paper
Author(s)
Type
Working Paper
Abstract
Federated learning (FL) has enabled training models collaboratively from
multiple data owning parties without sharing their data. Given the privacy
regulations of patient's healthcare data, learning-based systems in healthcare
can greatly benefit from privacy-preserving FL approaches. However, typical
model aggregation methods in FL are sensitive to local model updates, which may
lead to failure in learning a robust and accurate global model. In this work,
we implement and evaluate different robust aggregation methods in FL applied to
healthcare data. Furthermore, we show that such methods can detect and discard
faulty or malicious local clients during training. We run two sets of
experiments using two real-world healthcare datasets for training medical
diagnosis classification tasks. Each dataset is used to simulate the
performance of three different robust FL aggregation strategies when facing
different poisoning attacks. The results show that privacy preserving methods
can be successfully applied alongside Byzantine-robust aggregation techniques.
We observed in particular how using differential privacy (DP) did not
significantly impact the final learning convergence of the different
aggregation strategies.
multiple data owning parties without sharing their data. Given the privacy
regulations of patient's healthcare data, learning-based systems in healthcare
can greatly benefit from privacy-preserving FL approaches. However, typical
model aggregation methods in FL are sensitive to local model updates, which may
lead to failure in learning a robust and accurate global model. In this work,
we implement and evaluate different robust aggregation methods in FL applied to
healthcare data. Furthermore, we show that such methods can detect and discard
faulty or malicious local clients during training. We run two sets of
experiments using two real-world healthcare datasets for training medical
diagnosis classification tasks. Each dataset is used to simulate the
performance of three different robust FL aggregation strategies when facing
different poisoning attacks. The results show that privacy preserving methods
can be successfully applied alongside Byzantine-robust aggregation techniques.
We observed in particular how using differential privacy (DP) did not
significantly impact the final learning convergence of the different
aggregation strategies.
Date Issued
2020-09-17
Citation
2020
Publisher
arXiv
Copyright Statement
© 2020 The Author(s).
Sponsor
Commission of the European Communities
Identifier
http://arxiv.org/abs/2009.08294v1
Grant Number
319456
Subjects
cs.CR
cs.CR
Publication Status
Published