Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM

A crucial step to assure drug safety is predicting off-target binding. For oligonucleotide drugs this requires learning the relevant thermodynamics from often large-scale data distributed across different organisations. This process will respect data privacy if distributed and private learning under limited and private communication between local nodes is used. We propose an ADMM-based SVM with differential privacy for this purpose. We empirically show that this approach achieves accuracy comparable to the non-private one, i.e. similar to 86%, while yielding tight empirical privacy guarantees even after convergence.