Recently, federated learning has attracted much attention as a privacy-preserving integrated analysis that enables integrated analysis of data held by multiple institutions without sharing raw data. On the other hand, federated learning requires iterative communication across institutions and has a big challenge for implementation in situations where continuous communication with the outside world is extremely difficult. In this study, we propose a federated data collaboration learning (FedDCL), which solves such communication issues by combining federated learning with recently proposed non-model share-type federated learning named as data collaboration analysis. In the proposed FedDCL framework, each user institution independently constructs dimensionality-reduced intermediate representations and shares them with neighboring institutions on intra-group DC servers. On each intra-group DC server, intermediate representations are transformed to incorporable forms called collaboration representations. Federated learning is then conducted between intra-group DC servers. The proposed FedDCL framework does not require iterative communication by user institutions and can be implemented in situations where continuous communication with the outside world is extremely difficult. The experimental results show that the performance of the proposed FedDCL is comparable to that of existing federated learning.

翻译：近年来，联邦学习作为一种隐私保护的集成分析方法备受关注，它能够在无需共享原始数据的情况下对多个机构持有的数据进行集成分析。然而，联邦学习需要在机构间进行迭代通信，这对于在持续与外界通信极为困难的环境中实施带来了巨大挑战。本研究提出了一种联邦数据协作学习（FedDCL），通过将联邦学习与近期提出的非模型共享型联邦学习——数据协作分析相结合，解决了此类通信问题。在所提出的FedDCL框架中，每个用户机构独立构建降维的中间表示，并在组内数据协作服务器上将其共享给相邻机构。在每个组内数据协作服务器上，中间表示被转换为可整合的形式，称为协作表示。随后，在组内数据协作服务器之间进行联邦学习。所提出的FedDCL框架无需用户机构进行迭代通信，因而能够在持续与外界通信极为困难的环境中实施。实验结果表明，所提出的FedDCL性能与现有联邦学习相当。