Federated Learning of Molecular Properties in a Heterogeneous Setting
- URL: http://arxiv.org/abs/2109.07258v1
- Date: Wed, 15 Sep 2021 12:49:13 GMT
- Title: Federated Learning of Molecular Properties in a Heterogeneous Setting
- Authors: Wei Zhu, Andrew White, Jiebo Luo
- Abstract summary: We introduce federated heterogeneous molecular learning to address these challenges.
Federated learning allows end-users to build a global model collaboratively while preserving the training data distributed over isolated clients.
FedChem should enable a new type of collaboration for improving AI in chemistry that mitigates concerns about valuable chemical data.
- Score: 79.00211946597845
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 model collaboratively while
preserving the training data distributed over isolated clients. Due to the lack
of related research, we first simulate a federated heterogeneous benchmark
called FedChem. FedChem is constructed by jointly performing scaffold splitting
and Latent Dirichlet Allocation on existing datasets. Our results on FedChem
show that significant learning challenges arise when working with heterogeneous
molecules. We then propose a method to alleviate the problem, namely Federated
Learning by Instance reweighTing (FLIT). FLIT can align the local training
across heterogeneous clients by improving the performance for uncertain
samples. Comprehensive experiments conducted on our new benchmark FedChem
validate the advantages of this method over other federated learning schemes.
FedChem should enable a new type of collaboration for improving AI in chemistry
that mitigates concerns about valuable chemical data.
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