  Federated learning, i.e., a mobile edge computing framework for deep
learning, is a recent advance in privacy-preserving machine learning, where the
model is trained in a decentralized manner by the clients, i.e., data curators,
preventing the server from directly accessing those private data from the
clients. This learning mechanism significantly challenges the attack from the
server side. Although the state-of-the-art attacking techniques that
incorporated the advance of Generative adversarial networks (GANs) could
construct class representatives of the global data distribution among all
clients, it is still challenging to distinguishably attack a specific client
(i.e., user-level privacy leakage), which is a stronger privacy threat to
precisely recover the private data from a specific client. This paper gives the
first attempt to explore user-level privacy leakage against the federated
learning by the attack from a malicious server. We propose a framework
incorporating GAN with a multi-task discriminator, which simultaneously
discriminates category, reality, and client identity of input samples. The
novel discrimination on client identity enables the generator to recover user
specified private data. Unlike existing works that tend to interfere the
training process of the federated learning, the proposed method works
"invisibly" on the server side. The experimental results demonstrate the
effectiveness of the proposed attacking approach and the superior to the
state-of-the-art.
