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Federated Learning (FL) enables collaborative training among mutually distrusting parties. Model updates, rather than training data, are concentrated and fused in a central aggregation server. A key security challenge in FL is that an untrustworthy or compromised aggregation process might lead to unforeseeable information leakage. This challenge is especially acute due to recently demonstrated attacks that have reconstructed large fractions of training data from ostensibly "sanitized" modelarXiv:2105.09400v1 fatcat:ewbxs33eijfx5fsbmnsi62odl4