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Federated and decentralized machine learning leverage end-user devices forprivacy-preserving training of models at lower operating costs than within adata center. In a round of Federated Learning (FL), a random sample ofparticipants trains locally, then a central server aggregates the local modelsto produce a single model for the next round. In a round of DecentralizedLearning (DL), all participants train locally and then aggregate with theirimmediate neighbors, resulting in many local models with residual variancebetween them. On the one hand, FL's sampling and lower model variance provideslower communication costs and faster convergence. On the other hand, DL removesthe need for a central server and distributes the communication costs moreevenly amongst nodes, albeit at a larger total communication cost and slowerconvergence.  In this paper, we present MoDeST: Mostly-Consistent Decentralized SamplingTraining. MoDeST implements decentralized sampling in which a random subset ofnodes is responsible for training and aggregation every round: this providesthe benefits of both FL and DL without their traditional drawbacks. Ourevaluation of MoDeST on four common learning tasks: (i) confirms convergence asfast as FL, (ii) shows a 3x-14x reduction in communication costs compared toDL, and (iii) demonstrates that MoDeST quickly adapts to nodes joining,leaving, or failing, even when 80% of all nodes become unresponsive.

MoDeST: Bridging the Gap between Federated and Decentralized Learning with Decentralized Sampling