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Data Leakage in Federated Averaging. (arXiv:2206.12395v3 [cs.LG] UPDATED)
Nov. 2, 2022, 1:12 a.m. | Dimitar I. Dimitrov, Mislav Balunović, Nikola Konstantinov, Martin Vechev
cs.LG updates on arXiv.org arxiv.org
Recent attacks have shown that user data can be recovered from FedSGD
updates, thus breaking privacy. However, these attacks are of limited practical
relevance as federated learning typically uses the FedAvg algorithm. Compared
to FedSGD, recovering data from FedAvg updates is much harder as: (i) the
updates are computed at unobserved intermediate network weights, (ii) a large
number of batches are used, and (iii) labels and network weights vary
simultaneously across client steps. In this work, we propose a new …
More from arxiv.org / cs.LG updates on arXiv.org
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