The paper derives a per-client mutual-information bound for topology-aware leakage in differentially private federated learning and a min-max noise allocation that improves the bound over uniform noise when leverage scores are unequal.
Federated learning for predicting clinical outcomes in patients with covid-19,
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Topology-Aware Differential Privacy in Federated Learning
The paper derives a per-client mutual-information bound for topology-aware leakage in differentially private federated learning and a min-max noise allocation that improves the bound over uniform noise when leverage scores are unequal.