Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting. (arXiv:2109.07258v2 [cs.LG] UPDATED)

Chemistry research has both high material and computational costs to conduct
experiments. Institutions thus consider chemical data to be valuable and there
have been few efforts to construct large public datasets for machine learning.
Another challenge is that different intuitions are interested in different
classes of molecules, creating heterogeneous data that cannot be easily joined
by conventional distributed training. In this work, we introduce federated
heterogeneous molecular learning to address these challenges. Federated
learning allows end-users to build a global …

arxiv federated learning graph graph neural networks learning networks neural networks