We investigate the problem of private read update write (PRUW) with heterogeneous storage constrained databases in federated submodel learning (FSL). In FSL a machine learning (ML) model is divided into multiple submodels based on different types of data used to train it. A given user downloads, updates and uploads the updates back to a single submodel of interest, based on the type of user's local data. With PRUW, the process of reading (downloading) and writing (uploading) is carried out such that information theoretic privacy of the updating submodel index and the values of updates is guaranteed. We consider the practical scenario where the submodels are stored in databases with arbitrary (heterogeneous) storage constraints, and provide a PRUW scheme with a storage mechanism that utilizes submodel partitioning and encoding to minimize the communication cost.

翻译：我们研究了联邦子模型学习（FSL）中具有异构存储约束数据库的私有读写更新（PRUW）问题。在FSL中，机器学习（ML）模型根据训练所使用的不同数据类型被划分为多个子模型。给定用户根据其本地数据类型，下载、更新并上传单个目标子模型的更新内容。PRUW机制在读取（下载）与写入（上传）过程中，能够从信息论层面保证更新子模型索引及更新值的隐私性。我们考虑了实际场景中数据库具有任意（异构）存储约束的情况，提出了一种通过子模型分区与编码来优化通信成本的PRUW方案及其存储机制。