  Secure aggregation is a critical component in federated learning (FL), which
enables the server to learn the aggregate model of the users without observing
their local models. Conventionally, secure aggregation algorithms focus only on
ensuring the privacy of individual users in a single training round. We contend
that such designs can lead to significant privacy leakages over multiple
training rounds, due to partial user selection/participation at each round of
FL. In fact, we show that the conventional random user selection strategies in
FL lead to leaking users' individual models within number of rounds that is
linear in the number of users. To address this challenge, we introduce a secure
aggregation framework, Multi-RoundSecAgg, with multi-round privacy guarantees.
In particular, we introduce a new metric to quantify the privacy guarantees of
FL over multiple training rounds, and develop a structured user selection
strategy that guarantees the long-term privacy of each user (over any number of
training rounds). Our framework also carefully accounts for the fairness and
the average number of participating users at each round. Our experiments on
MNIST and CIFAR-10 datasets in the IID and the non-IID settings demonstrate the
performance improvement over the baselines, both in terms of privacy protection
and test accuracy.
