SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated
  Learning
Tanguy Marchand Régis Loeb Ulysse Marteau-Ferey Jean Ogier du Terrail Arthur Pignet
We consider a cross-silo federated learning (FL) setting where a machine
learning model with a fully connected first layer is trained between different
clients and a central server using FedAvg, and where the aggregation step can
be performed with secure aggregation (SA). We present SRATTA an attack relying
only on aggregated models which, under realistic assumptions, (i) recovers data
samples from the different clients, and (ii) groups data samples coming from
the same client together. While sample recovery has already been explored in an
FL setting, the ability to group samples per client, despite the use of SA, is
novel. This poses a significant unforeseen security threat to FL and
effectively breaks SA. We show that SRATTA is both theoretically grounded and
can be used in practice on realistic models and datasets. We also propose
counter-measures, and claim that clients should play an active role to
guarantee their privacy during training.