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Efficient Variance-Reduced Learning for Fully Decentralized On-Device Intelligence. (arXiv:1708.01384v1 [cs.LG])
来源于:arXiv
This work develops a fully decentralized variance-reduced learning algorithm
for on-device intelligence where nodes store and process the data locally and
are only allowed to communicate with their immediate neighbors. In the proposed
algorithm, there is no need for a central or master unit while the objective is
to enable the dispersed nodes to learn the {\em exact} global model despite
their limited localized interactions. The resulting algorithm is shown to have
low memory requirement, guaranteed linear convergence, robustness to failure of
links or nodes, scalability to the network size, and privacy-preserving
properties. Moreover, the decentralized nature of the solution makes
large-scale machine learning problems more tractable and also scalable since
data is stored and processed locally at the nodes. 查看全文>>