Federated learning, in which training data is distributed among users and
never shared, has emerged as a popular approach to privacy-preserving machine
learning. Cryptographic techniques such as secure aggregation are used to
aggregate contributions, like a model update, from all users. A robust
technique for making such aggregates differentially private is to exploit
infinite divisibility of the Laplace distribution, namely, that a Laplace
distribution can be expressed as a sum of i.i.d. noise shares from a Gamma
distribution, one share added by each user.
However, Laplace noise is known to have suboptimal error in the low privacy
regime for $varepsilon$-differential privacy, where $varepsilon > 1$ is a
large constant. In this paper we present the first infinitely divisible noise
distribution for real-valued data that achieves $varepsilon$-differential
privacy and has expected error that decreases exponentially with $varepsilon$.