Efficient Ring-Topology Decentralized Federated Learning with Deep Generative Models for Medical Data in eHealthcare Systems

Electronics

Shiyang Yan¹  Ruijie Hou²  Yifan Hu²  Chao Wu²  Zhao Wang²  Zhihao Wang² 

[1] Inria, 78150 Le Chesnay-Rocquencourt, France;School of Public Affairs, Zhejiang University, Hangzhou 310027, China;

关键词: federated learning;    medical data security;    ring topology;    deep generative model;    privacy preserving;    non-IID;   

DOI  :  10.3390/electronics11101548

来源: DOAJ

【 摘 要 】

By leveraging deep learning technologies, data-driven-based approaches have reached great success with the rapid increase of data generated for medical applications. However, security and privacy concerns are obstacles for data providers in many sensitive data-driven scenarios, such as rehabilitation and 24 h on-the-go healthcare services. Although many federated learning (FL) approaches have been proposed with DNNs for medical applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topology-based decentralized federated learning (RDFL) scheme for deep generative models (DGM), where DGM is a promising solution for solving the aforementioned data usability issues. Our RDFL schemes provide communication efficiency and maintain training performance to boost DGMs in target tasks compared with existing FL works. A novel ring FL topology and a map-reduce-based synchronizing method are designed in the proposed RDFL to improve the decentralized FL performance and bandwidth utilization. In addition, an inter-planetary file system (IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstrate the superiority of RDFL with either independent and identically distributed (IID) datasets or non-independent and identically distributed (Non-IID) datasets.

【 授权许可】

Unknown