Federated Learning on Transcriptomic Data: Model Quality and Performance Trade-Offs

arXiv:2402.14527v1 Announce Type: cross
Abstract: Machine learning on large-scale genomic or transcriptomic data is important for many novel health applications. For example, precision medicine tailors medical treatments to patients on the basis of individual biomarkers, cellular and molecular states, etc. However, the data required is sensitive, voluminous, heterogeneous, and typically distributed across locations where dedicated machine learning hardware is not available. Due to privacy and regulatory reasons, it is also problematic to aggregate all data at a trusted third party.Federated …

applications arxiv cellular cs.cr cs.lg data etc federated federated learning health important large machine machine learning medical medicine novel patients performance quality scale sensitive states trade trade-offs