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An Accurate, Scalable and Verifiable Protocol for Federated Differentially Private Averaging
[article]
2022
arXiv
pre-print
Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of federated learning algorithms. Our first contribution is a scalable protocol in which participants exchange correlated Gaussian noise along the edges of a network graph,
arXiv:2006.07218v3
fatcat:s2ppenwa6jc4letfpzzpzhlamy