Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors

• URL: http://arxiv.org/abs/2112.06872v1
• Date: Mon, 13 Dec 2021 18:31:08 GMT
• Title: Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors
• Authors: Timothy Stevens, Christian Skalka, Christelle Vincent, John Ring, Samuel Clark, Joseph Near
• Abstract summary: Federated machine learning leverages edge computing to develop models from network user data. Privacy in federated learning remains a major challenge. Recent advances in emphsecure aggregation using multiparty computation eliminate the need for a third party. We present a new federated learning protocol that leverages a novel differentially private, malicious secure aggregation protocol.
• Score: 1.4680035572775534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy have been proposed to address this, but bring their own challenges -- many require a trusted third party or else add too much noise to produce useful models. Recent advances in \emph{secure aggregation} using multiparty computation eliminate the need for a third party, but are computationally expensive especially at scale. We present a new federated learning protocol that leverages a novel differentially private, malicious secure aggregation protocol based on techniques from Learning With Errors. Our protocol outperforms current state-of-the art techniques, and empirical results show that it scales to a large number of parties, with optimal accuracy for any differentially private federated learning scheme.

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