With the exponential growth of data created at the network edge, decentralized and Gossip-based training of deep learning (DL) models on edge computing (EC) gains tremendous research momentum, owing to its capability to learn from resource-strenuous edge nodes with limited network connectivity. Today's edge devices are extremely heterogeneous, e.g., hardware and software stacks, and result in high performance variation of training time and inducing extra delay to synchronize and converge. The large body of prior art accelerates DL, being data or model parallelization, via a centralized server, e.g., parameter server scheme, which may easily turn into the system bottleneck or single point of failure. In this artice, we propose EdgeGossip, a framework specifically designed to accelerate the training process of decentralized and Gossip-based DL training for heterogeneous EC platforms. EdgeGossip features on: (i) low performance variation among multiple EC platforms during iterative training, and (ii) accuracy-aware training to fastly obtain best possible model accuracy. We implement EdgeGossip based on popular Gossip algorithms and demonstrate its effectiveness using real-world DL workloads, i.e., considerably reducing model training time by an average of 2.70 times while only incurring accuracy losses of 0.78 percent.

The core of many large-scale machine learning (ML) applications, such as neural networks (NN), support vector machine (SVM), and convolutional neural network (CNN), is the training algorithm that iteratively updates model parameters by processing massive datasets. From a plethora of studies aiming at accelerating ML, being data parallelization and parameter server, the prevalent assumption is that all data points are equivalently relevant to model parameter updating. In this article, we challenge this assumption by proposing a criterion to measure a data point's effect on model parameter updating, and experimentally demonstrate that the majority of data points are non-critical in the training process. We develop a slim learning framework, termed SlimML, which trains the ML models only on the critical data and thus significantly improves training performance. To such an end, SlimML efficiently leverages a small number of aggregated data points per iteration to approximate the criticalness of original input data instances. The proposed approach can be used by changing a few lines of code in a standard stochastic gradient descent (SGD) procedure, and we demonstrate experimentally, on NN regression, SVM classification, and CNN training, that for large datasets, it accelerates model training process by an average of 3.61 times while only incurring accuracy losses of 0.37 percent.