Decomposable Submodular Maximization in Federated Setting

Submodular functions, as well as the sub-class of decomposable submodular functions, and their optimization appear in a wide range of applications in machine learning, recommendation systems, and welfare maximization. However, optimization of decomposable submodular functions with millions of component functions is computationally prohibitive. Furthermore, the component functions may be private (they might represent user preference function, for example) and cannot be widely shared. To address these issues, we propose a {\em federated optimization} setting for decomposable submodular optimization. In this …

applications class cs.ai cs.dc cs.ds cs.lg functions machine machine learning optimization recommendation recommendation systems systems welfare