FedHealth 2: Weighted Federated Transfer Learning via Batch
Normalization for Personalized Healthcare
- URL: http://arxiv.org/abs/2106.01009v1
- Date: Wed, 2 Jun 2021 08:10:50 GMT
- Title: FedHealth 2: Weighted Federated Transfer Learning via Batch
Normalization for Personalized Healthcare
- Authors: Yiqiang Chen, Wang Lu, Jindong Wang, Xin Qin
- Abstract summary: FedHealth 2 is an extension of FedHealth to tackle domain shifts and get personalized models for local clients.
It can achieve better accuracy (10%+ improvement for activity recognition) and personalized healthcare without compromising privacy and security.
- Score: 10.350441801743855
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The success of machine learning applications often needs a large quantity of
data. Recently, federated learning (FL) is attracting increasing attention due
to the demand for data privacy and security, especially in the medical field.
However, the performance of existing FL approaches often deteriorates when
there exist domain shifts among clients, and few previous works focus on
personalization in healthcare. In this article, we propose FedHealth 2, an
extension of FedHealth \cite{chen2020fedhealth} to tackle domain shifts and get
personalized models for local clients. FedHealth 2 obtains the client
similarities via a pretrained model, and then it averages all weighted models
with preserving local batch normalization. Wearable activity recognition and
COVID-19 auxiliary diagnosis experiments have evaluated that FedHealth 2 can
achieve better accuracy (10%+ improvement for activity recognition) and
personalized healthcare without compromising privacy and security.
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