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Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors
[article]
2021
arXiv
pre-print
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
arXiv:2112.06872v1
fatcat:k2cbkzojcvfybabfhzttk6gk2i