title: Asynchronous Decentralized SGD with Quantized and Local Updates
abstract: Decentralized optimization is emerging as a viable alternative for scalable distributed machine learning, 
  but also introduces new challenges in terms of synchronization costs.  
  To this end, several communication-reduction techniques, such as non-blocking communication, quantization, and local steps, 
  have been explored in the decentralized setting. 
  Due to the complexity of analyzing optimization in such a relaxed setting, 
  this line of work often assumes \emph{global} communication rounds, which require additional synchronization. 
  In this paper, we consider decentralized optimization in the simpler, but harder to analyze, \emph{asynchronous gossip} model, 
  in which communication occurs in discrete, randomly chosen pairings among nodes. 
  Perhaps surprisingly, we show that a variant of SGD called \emph{SwarmSGD} still converges in this setting, 
  even if \emph{non-blocking communication}, \emph{quantization}, and \emph{local steps} are all applied \emph{in conjunction}, and even if the node data distributions and underlying graph topology are both \emph{heterogenous}. 
  Our analysis is based on a new connection with multi-dimensional load-balancing processes. 
  We implement this algorithm and deploy it in a super-computing environment, showing that it can outperform previous decentralized methods in terms of end-to-end training time, and that it can even rival carefully-tuned large-batch SGD for certain tasks. 
authors: Giorgi Nadiradze Amirmojtaba Sabour Peter Davies Shigang Li Dan Alistarh
hIndex: Dan Alistarh 26