Quality Inference in Federated Learning with Secure Aggregation. (arXiv:2007.06236v4 [cs.LG] UPDATED)

Federated learning algorithms are developed both for efficiency reasons and
to ensure the privacy and confidentiality of personal and business data,
respectively. Despite no data being shared explicitly, recent studies showed
that the mechanism could still leak sensitive information. Hence, secure
aggregation is utilized in many real-world scenarios to prevent attribution to
specific participants. In this paper, we focus on the quality of individual
training datasets and show that such quality information could be inferred and
attributed to specific participants even when secure aggregation is applied.
Specifically, through a series of image recognition experiments, we infer the
relative quality ordering of participants. Moreover, we apply the inferred
quality information to detect misbehaviours, to stabilize training performance,
and to measure the individual contributions of participants.