  The distributed optimization problem has become increasingly relevant
recently. It has a lot of advantages such as processing a large amount of data
in less time compared to non-distributed methods. However, most distributed
approaches suffer from a significant bottleneck - the cost of communications.
Therefore, a large amount of research has recently been directed at solving
this problem. One such approach uses local data similarity. In particular,
there exists an algorithm provably optimally exploiting the similarity
property. But this result, as well as results from other works solve the
communication bottleneck by focusing only on the fact that communication is
significantly more expensive than local computing and does not take into
account the various capacities of network devices and the different
relationship between communication time and local computing expenses. We
consider this setup and the objective of this study is to achieve an optimal
ratio of distributed data between the server and local machines for any costs
of communications and local computations. The running times of the network are
compared between uniform and optimal distributions. The superior theoretical
performance of our solutions is experimentally validated.
