Decentralized Deep Learning with Arbitrary Communication Compression
This addresses bandwidth constraints for decentralized training in scenarios like data privacy and large-scale computing, offering incremental improvements over prior methods.
The paper tackles the problem of limited bandwidth in decentralized deep learning training by proposing communication compression that works under arbitrary high compression ratios for non-convex functions, achieving a convergence rate of O(1/√(nT)) and demonstrating practical speedups in distributed user devices and datacenters.
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. We show that Choco-SGD $-$ recently introduced and analyzed for strongly-convex objectives only $-$ converges under arbitrary high compression ratio on general non-convex functions at the rate $O\bigl(1/\sqrt{nT}\bigr)$ where $T$ denotes the number of iterations and $n$ the number of workers. The algorithm achieves linear speedup in the number of workers and supports higher compression than previous state-of-the art methods. We demonstrate the practical performance of the algorithm in two key scenarios: the training of deep learning models (i) over distributed user devices, connected by a social network and (ii) in a datacenter (outperforming all-reduce time-wise).