  Machine learning makes multimedia data (e.g., images) more attractive,
however, multimedia data is usually distributed and privacy sensitive. Multiple
distributed multimedia clients can resort to federated learning (FL) to jointly
learn a global shared model without requiring to share their private samples
with any third-party entities. In this paper, we show that FL suffers from the
cross-client generative adversarial networks (GANs)-based (C-GANs) attack, in
which a malicious client (i.e., adversary) can reconstruct samples with the
same distribution as the training samples from other clients (i.e., victims).
Since a benign client's data can be leaked to the adversary, this attack brings
the risk of local data leakage for clients in many security-critical FL
applications. Thus, we propose Fed-EDKD (i.e., Federated Ensemble Data-free
Knowledge Distillation) technique to improve the current popular FL schemes to
resist C-GANs attack. In Fed-EDKD, each client submits a local model to the
server for obtaining an ensemble global model. Then, to avoid model expansion,
Fed-EDKD adopts data-free knowledge distillation techniques to transfer
knowledge from the ensemble global model to a compressed model. By this way,
Fed-EDKD reduces the adversary's control capability over the global model, so
Fed-EDKD can effectively mitigate C-GANs attack. Finally, the experimental
results demonstrate that Fed-EDKD significantly mitigates C-GANs attack while
only incurring a slight accuracy degradation of FL.
