Gradient Obfuscation Gives a False Sense of Security in Federated Learning. (arXiv:2206.04055v1 [cs.CR])

Federated learning has been proposed as a privacy-preserving machine learning
framework that enables multiple clients to collaborate without sharing raw
data. However, client privacy protection is not guaranteed by design in this
framework. Prior work has shown that the gradient sharing strategies in
federated learning can be vulnerable to data reconstruction attacks. In
practice, though, clients may not transmit raw gradients considering the high
communication cost or due to privacy enhancement requirements. Empirical
studies have demonstrated that gradient obfuscation, including …

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