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.

翻译：最近,联合会学习(FL)越来越受欢迎,因为它使研究人员能够在不损害数据隐私和安全的情况下培训强大的模型;然而,在遇到非二类情况时,如果客户之间的分配存在差距,现有FL方法的绩效往往会恶化,而以前的努力很少侧重于保健方面的个性化;在本篇文章中,我们建议FedAP处理域变换,然后为当地客户获得个性化模式;FedAP根据批次正常化层次的统计了解客户之间的相似性,同时保持每个客户与当地不同组别的特殊性;对五种保健基准的全面实验表明,FedAP比最先进的方法(例如PAMAP2的精确度提高10%)更快地达到更高的准确性。