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Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed data sources through an interactive process of local computation and message passing.
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2018
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——, “Recycled admm: Improve privacy and accuracy with less computation in distributed algorithms,” in 2018 56th Annual Allerton Conference on Communication, Control, and Computing . IEEE, 2018, pp. 959–965
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J. Ding, S. M. Errapotu, H. Zhang, Y. Gong, M. Pan, and Z. Han, “Stochastic admm based distributed machine learning with differential privacy,” to appear in SecureComm. EAI, 2019
2019
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