  Coded computing is a method for mitigating straggling workers in a
centralized computing network, by using erasure-coding techniques. Federated
learning is a decentralized model for training data distributed across client
devices. In this work we propose approximating the inverse of an aggregated
data matrix, where the data is generated by clients; similar to the federated
learning paradigm, while also being resilient to stragglers. To do so, we
propose a coded computing method based on gradient coding. We modify this
method so that the coordinator does not access the local data at any point;
while the clients access the aggregated matrix in order to complete their
tasks. The network we consider is not centrally administrated, and the
communications which take place are secure against potential eavesdroppers.
