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Differential Privacy of Decentralized Learning: Centralized Rate Conditions and Nearly Tight Guarantees
We analyze the differential privacy of gossip averaging on a graph with independent Gaussian noise at every node, as seen by a subset of observing nodes. We characterize the privacy loss of any sensitive node over multiple gossip steps as that of a Gaussian mechanism whose sensitivity is the solutio
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