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
Published: 2021-12-13

Source: https://arxiv.org/abs/2112.06872

PDF: https://arxiv.org/pdf/2112.06872

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プライバシー侵害 通信効率 プロトコル性能評価

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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 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.