  We propose a privacy-preserving federated learning (FL) scheme that is
resilient against straggling devices. An adaptive scenario is suggested where
the slower devices share their data with the faster ones and do not participate
in the learning process. The proposed scheme employs code-based cryptography to
ensure \emph{computational} privacy of the private data, i.e., no device with
bounded computational power can obtain information about the other devices'
data in feasible time. For a scenario with 25 devices, the proposed scheme
achieves a speed-up of 4.7 and 4 for 92 and 128 bits security, respectively,
for an accuracy of 95\% on the MNIST dataset compared with conventional
mini-batch FL.
