Personalized Federated Learning with Adaptive Batchnorm for Healthcare
It addresses domain shifts and personalization for healthcare applications, offering incremental improvements over existing federated learning methods.
The paper tackles the problem of performance deterioration in federated learning due to non-iid data distributions in healthcare by proposing FedAP, which uses adaptive batch normalization for personalization, resulting in up to 10% accuracy improvement on benchmarks like PAMAP2.
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.