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Collaborative Deep Learning Across Multiple Data Centers
[article]
2018
arXiv
pre-print
Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by
arXiv:1810.06877v1
fatcat:xrvexxzg2rdbffdnwgbsikaw2u