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 in medicine: fa- cilitating multi-institutional collaborations without sharing patient data,
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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.