Personalized Federated Learning With Adaptive Batchnorm for Healthcare

There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist distribution gaps among clients, and few previous efforts focus on personalization in healthcare. In this article, we propose FedAP to tackle domain shifts and then obtain personalized models for local clients. FedAP learns the similarity between clients based on the statistics of the batch normalization layers while preserving the specificity of each client with different local batch normalization. Comprehensive experiments on five healthcare benchmarks demonstrate that FedAP achieves better accuracy compared to state-of-the-art methods (e.g., 10%+ accuracy improvement for PAMAP2) with faster convergence speed.

1 Introduction

Machine learning has been widely adopted in many applications in people's daily life [1], [2], [3]. Specifically for healthcare, researchers can build models to predict health status by leveraging health-related data, such as activity sensors [4], images [5], and other health information [6], [7], [8]. To achieve satisfying performance, machine learning healthcare applications often require sufficient client data for model training. However, with the increasing awareness of privacy and security, more governments and organizations enforce the protection of personal data via different regulations [9], [10]. In this situation, federated learning (FL) [11] emerges to build powerful machine learning models with data privacy well-protected.