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Quantization enabled Privacy Protection in Decentralized Stochastic Optimization. (arXiv:2208.04845v1 [math.OC])
Aug. 10, 2022, 1:20 a.m. | Yongqiang Wang, Tamer Basar
cs.CR updates on arXiv.org arxiv.org
By enabling multiple agents to cooperatively solve a global optimization
problem in the absence of a central coordinator, decentralized stochastic
optimization is gaining increasing attention in areas as diverse as machine
learning, control, and sensor networks. Since the associated data usually
contain sensitive information, such as user locations and personal identities,
privacy protection has emerged as a crucial need in the implementation of
decentralized stochastic optimization. In this paper, we propose a
decentralized stochastic optimization algorithm that is able to …
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