  In cross-device federated learning (FL) setting, clients such as mobiles
cooperate with the server to train a global machine learning model, while
maintaining their data locally. However, recent work shows that client's
private information can still be disclosed to an adversary who just eavesdrops
the messages exchanged between the client and the server. For example, the
adversary can infer whether the client owns a specific data instance, which is
called a passive membership inference attack. In this paper, we propose a new
passive inference attack that requires much less computation power and memory
than existing methods. Our empirical results show that our attack achieves a
higher accuracy on CIFAR100 dataset (more than $4$ percentage points) with
three orders of magnitude less memory space and five orders of magnitude less
calculations.
