Optimal Data Splitting in Distributed Optimization for Machine Learning

arXiv:2401.07809v2 Announce Type: replace-cross
Abstract: 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 …

abstract advantages arxiv become communications cost cs.lg data distributed however machine machine learning math.oc optimization processing type