  Optimization in distributed networks plays a central role in almost all
distributed machine learning problems. In principle, the use of distributed
task allocation has reduced the computational time, allowing better response
rates and higher data reliability. However, for these computational algorithms
to run effectively in complex distributed systems, the algorithms ought to
compensate for communication asynchrony, network node failures and delays known
as stragglers. These issues can change the effective connection topology of the
network, which may vary over time, thus hindering the optimization process. In
this paper, we propose a new distributed unconstrained optimization algorithm
for minimizing a convex function which is adaptable to a parameter server
network. In particular, the network worker nodes solve their local optimization
problems, allowing the computation of their local coded gradients, which will
be sent to different server nodes. Then within this parameter server platform
each server node aggregates its communicated local gradients, allowing
convergence to the desired optimizer. This algorithm is robust to network s
worker node failures, disconnection, or delaying nodes known as stragglers. One
way to overcome the straggler problem is to allow coding over the network. We
further extend this coding framework to enhance the convergence of the proposed
algorithm under such varying network topologies. By using coding and utilizing
evaluations of gradients of uniformly bounded delay we further enhance the
proposed algorithm performance. Finally, we implement the proposed scheme in
MATLAB and provide comparative results demonstrating the effectiveness of the
proposed framework
