  Federated learning (FL) is an emerging distributed machine learning framework
for collaborative model training with a network of clients (edge devices). FL
offers default client privacy by allowing clients to keep their sensitive data
on local devices and to only share local training parameter updates with the
federated server. However, recent studies have shown that even sharing local
parameter updates from a client to the federated server may be susceptible to
gradient leakage attacks and intrude the client privacy regarding its training
data. In this paper, we present a principled framework for evaluating and
comparing different forms of client privacy leakage attacks. We first provide
formal and experimental analysis to show how adversaries can reconstruct the
private local training data by simply analyzing the shared parameter update
from local training (e.g., local gradient or weight update vector). We then
analyze how different hyperparameter configurations in federated learning and
different settings of the attack algorithm may impact on both attack
effectiveness and attack cost. Our framework also measures, evaluates, and
analyzes the effectiveness of client privacy leakage attacks under different
gradient compression ratios when using communication efficient FL protocols.
Our experiments also include some preliminary mitigation strategies to
highlight the importance of providing a systematic attack evaluation framework
towards an in-depth understanding of the various forms of client privacy
leakage threats in federated learning and developing theoretical foundations
for attack mitigation.
