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Current deep learning (DL) systems rely on a centralized computing paradigmwhich limits the amount of available training data, increases system latency,and adds privacy and security constraints. On-device learning, enabled bydecentralized and distributed training of DL models over peer-to-peerwirelessly connected edge devices, not only alleviate the above limitations butalso enable next-gen applications that need DL models to continuously interactand learn from their environment. However, this necessitates the development ofnovel training algorithms that train DL models over time-varying and directedpeer-to-peer graph structures while minimizing the amount of communicationbetween the devices and also being resilient to non-IID data distributions. Inthis work we propose, Sparse-Push, a communication efficient decentralizeddistributed training algorithm that supports training over peer-to-peer,directed, and time-varying graph topologies. The proposed algorithm enables466x reduction in communication with only 1% degradation in performance whentraining various DL models such as ResNet-20 and VGG11 over the CIFAR-10dataset. Further, we demonstrate how communication compression can lead tosignificant performance degradation in-case of non-IID datasets, and proposeSkew-Compensated Sparse Push algorithm that recovers this performance dropwhile maintaining similar levels of communication compression.

Sparse-Push: Communication- & Energy-Efficient Decentralized Distributed Learning over Directed & Time-Varying Graphs with non-IID Datasets