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Eluding Secure Aggregation in Federated Learning via Model Inconsistency. (arXiv:2111.07380v3 [cs.LG] UPDATED)
May 4, 2022, 1:20 a.m. | Dario Pasquini, Danilo Francati, Giuseppe Ateniese
cs.CR updates on arXiv.org arxiv.org
Secure aggregation is a cryptographic protocol that securely computes the
aggregation of its inputs. It is pivotal in keeping model updates private in
federated learning. Indeed, the use of secure aggregation prevents the server
from learning the value and the source of the individual model updates provided
by the users, hampering inference and data attribution attacks.
In this work, we show that a malicious server can easily elude secure
aggregation as if the latter were not in place. We devise …
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