Introduces federated differential privacy as an intermediate model between local and central DP and analyzes minimax rates for four statistical tasks under heterogeneity and privacy.
For E ′ 2, the same arguments for controlling E2 in Lemma 11 still work, but with n by N in the choice of R to account for the union bound over N random variables
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Federated Transfer Learning with Differential Privacy
Introduces federated differential privacy as an intermediate model between local and central DP and analyzes minimax rates for four statistical tasks under heterogeneity and privacy.