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Client-specific Property Inference against Secure Aggregation in Federated Learning. (arXiv:2303.03908v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
Federated learning has become a widely used paradigm for collaboratively
training a common model among different participants with the help of a central
server that coordinates the training. Although only the model parameters or
other model updates are exchanged during the federated training instead of the
participant's data, many attacks have shown that it is still possible to infer
sensitive information such as membership, property, or outright reconstruction
of participant data. Although differential privacy is considered an effective
solution to …
aggregation attacks client data federated learning information paradigm sensitive information server training updates