  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 fully-homomorphic 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
framework to train a deep learning model to predict a person's age from
distributed MRI scans, a common benchmarking task, and demonstrate that there
is no degradation in the learning performance between the encrypted and
non-encrypted federated models.
