Decentralized Deep Learning with Arbitrary Communication Compression

ICLR 2020 Anastasia KoloskovaTao LinSebastian U. StichMartin Jaggi

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches suffer from limited bandwidth of the network, we propose the use of communication compression in the decentralized training context... (read more)

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