Fingerprint Attack: Client De-Anonymization in Federated Learning
Qiongka Xu,
Trevor Cohn,
Olga Ohrimenko
Abstract:Federated Learning allows collaborative training without data sharing in settings where participants do not trust the central server and one another. Privacy can be further improved by ensuring that communication between the participants and the server is anonymized through a shuffle; decoupling the participant identity from their data. This paper seeks to examine whether such a defense is adequate to guarantee anonymity, by proposing a novel fingerprinting attack over gradients sent by the participants to the… Show more
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