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Differentially Private Federated Learning without Noise Addition: When is it Possible?
May 9, 2024, 4:11 a.m. | Jiang Zhang, Yahya H Ezzeldin, Ahmed Roushdy Elkordy, Konstantinos Psounis, Salman Avestimehr
cs.CR updates on arXiv.org arxiv.org
Abstract: Federated Learning (FL) with Secure Aggregation (SA) has gained significant attention as a privacy preserving framework for training machine learning models while preventing the server from learning information about users' data from their individual encrypted model updates. Recent research has extended privacy guarantees of FL with SA by bounding the information leakage through the aggregate model over multiple training rounds thanks to leveraging the "noise" from other users' updates. However, the privacy metric used in …
addition aggregation arxiv attention cs.cr cs.lg data encrypted federated federated learning framework information machine machine learning machine learning models noise privacy privacy preserving private research server training updates
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