AIDE: Fast and Communication Efficient Distributed Optimization

Understand

In this paper, we present two new communication-efficient methods for distributed minimization of an average of functions.

• The first algorithm is an inexact variant of the DANE algorithm that allows any local algorithm to return an approximate solution to a local subproblem.
• We show that such a strategy does not affect the theoretical guarantees of DANE significantly.
• In fact, our approach can be viewed as a robustification strategy since the method is substantially better behaved than DANE on data partition arising in practice.