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Federated Stochastic Primal-dual Learning with Differential Privacy. (arXiv:2204.12284v1 [cs.LG])
April 27, 2022, 1:20 a.m. | Yiwei Li, Shuai Wang, Tsung-Hui Chang, Chong-Yung Chi
cs.CR updates on arXiv.org arxiv.org
Federated learning (FL) is a new paradigm that enables many clients to
jointly train a machine learning (ML) model under the orchestration of a
parameter server while keeping the local data not being exposed to any third
party. However, the training of FL is an interactive process between local
clients and the parameter server. Such process would cause privacy leakage
since adversaries may retrieve sensitive information by analyzing the overheard
messages. In this paper, we propose a new federated stochastic …
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