  We present two novel federated learning (FL) schemes that mitigate the effect
of straggling devices by introducing redundancy on the devices' data across the
network. Compared to other schemes in the literature, which deal with
stragglers or device dropouts by ignoring their contribution, the proposed
schemes do not suffer from the client drift problem. The first scheme,
CodedPaddedFL, mitigates the effect of stragglers while retaining the privacy
level of conventional FL. It combines one-time padding for user data privacy
with gradient codes to yield straggler resiliency. The second scheme,
CodedSecAgg, provides straggler resiliency and robustness against model
inversion attacks and is based on Shamir's secret sharing. We apply
CodedPaddedFL and CodedSecAgg to a classification problem. For a scenario with
120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of
95% on the MNIST dataset compared to conventional FL. Furthermore, it yields
similar performance in terms of latency compared to a recently proposed scheme
by Prakash et al. without the shortcoming of additional leakage of private
data. CodedSecAgg outperforms the state-of-the-art secure aggregation scheme
LightSecAgg by a speed-up factor of 6.6-18.7 for the MNIST dataset for an
accuracy of 95%.
