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Distributed deep learning (DDL) training systems are designed for cloud anddata-center environments that assumes homogeneous compute resources, highnetwork bandwidth, sufficient memory and storage, as well as independent andidentically distributed (IID) data across all nodes. However, these assumptionsdon't necessarily apply on the edge, especially when training neural networkson streaming data in an online manner. Computing on the edge suffers from bothsystems and statistical heterogeneity. Systems heterogeneity is attributed todifferences in compute resources and bandwidth specific to each device, whilestatistical heterogeneity comes from unbalanced and skewed data on the edge.Different streaming-rates among devices can be another source of heterogeneitywhen dealing with streaming data. If the streaming rate is lower than trainingbatch-size, device needs to wait until enough samples have streamed in beforeperforming a single iteration of stochastic gradient descent (SGD). Thus,low-volume streams act like stragglers slowing down devices with high-volumestreams in synchronous training. On the other hand, data can accumulate quicklyin the buffer if the streaming rate is too high and the devices can't train atline-rate. In this paper, we introduce ScaDLES to efficiently train onstreaming data at the edge in an online fashion, while also addressing thechallenges of limited bandwidth and training with non-IID data. We empiricallyshow that ScaDLES converges up to 3.29 times faster compared to conventionaldistributed SGD.

ScaDLES: Scalable Deep Learning over Streaming data at the Edge