Abstract

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location. This results in an improved generalizability of models and efficient scaling of computation as more sources and larger datasets are added to the federation. Nevertheless, recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site, requiring improved security solutions. In this work, we propose a framework for secure FL using fullyhomomorphic encryption (FHE). Specifically, we use the CKKS construction, an approximate, floating point compatible scheme that benefits from ciphertext packing and rescaling. In our evaluation on large-scale brain MRI datasets, we use our proposed secure FL …

Metadata

publication

17th International Symposium on Medical Information Processing and Analysis …, 2021

year

2021

publication date

2021/12/10

authors

Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M Thompson, Jose Luis Ambite

link

https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12088/1208814/Secure-neuroimaging-analysis-using-federated-learning-with-homomorphic-encryption/10.1117/12.2606256.short

resource_link

https://arxiv.org/pdf/2108.03437

conference

17th international symposium on medical information processing and analysis

volume

12088

pages

351-359

publisher

SPIE