We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization (EM) over latent master's distribution and clients' parameters. We also introduce formal differential privacy (DP) guarantees compatibly with our EM optimization scheme. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners, even when local parameters perturbation is included to provide DP guarantees. Moreover, the variability of data, views and centers can be quantified in an interpretable manner, while guaranteeing high-quality data reconstruction as compared to state-of-the-art autoencoding models and federated learning schemes. The code is available at https://gitlab.inria.fr/epione/federated-multi-views-ppca.
翻译:我们提出一个新的联邦学习范式,以模拟多中心研究中不同客户的数据变异性。我们的方法通过一种等级化的巴耶斯潜伏变量模型来表达。根据这种模型,假设客户特有的参数是从总体一级全球分布中实现的,而根据数据偏差和不同客户的变异性进行估计。我们表明,通过预期最大化(EM),而不是潜在主分布和客户参数,我们的框架可以有效地优化。我们还引入了与我们的EM优化计划相兼容的正式差异隐私保障(DP) 。我们测试了我们分析多模式医学成像数据的方法,以及分布的受阿尔茨海默氏病影响的病人临床数据集的临床分数。我们证明,当数据以iid和非二种方式分布时,我们的方法是稳健的,即使地方参数渗透性能提供DP保证。此外,数据、观点和中心的变化性可以以可解释的方式量化,同时保证与州-艺术自动co化模型和联邦化学习计划相比高质量的数据重建。我们证明,当数据以iddd-marviews/grement-frial-formarial-friament.