Federated Learning (FL) is an exciting new paradigm that enables training a
global model from data generated locally at the client nodes, without moving
client data to a centralized server. Performance of FL in a multi-access edge
computing (MEC) network suffers from slow convergence due to heterogeneity and
stochastic fluctuations in compute power and communication link qualities
across clients. A recent work, Coded Federated Learning (CFL), proposes to
mitigate stragglers and speed up training for linear regression tasks by
assigning redundant computations at the MEC server. Coding redundancy in CFL is
computed by exploiting statistical properties of compute and communication
delays. We develop CodedFedL that addresses the difficult task of extending CFL
to distributed non-linear regression and classification problems with
multioutput labels. The key innovation of our work is to exploit distributed
kernel embedding using random Fourier features that transforms the training
task into distributed linear regression. We provide an analytical solution for
load allocation, and demonstrate significant performance gains for CodedFedL
through experiments over benchmark datasets using practical network parameters.

CodedFedL extends CFL to distributed non-linear regression and classification problems with multioutput labels by exploiting distributed kernel embedding using random Fourier features that transforms the training task into distributed linear regression, providing an analytical solution for load allocation and significant performance gains over benchmark datasets using practical network parameters.