# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 4 Experiments and Results
### The Dynamics of the Fault Tolerance  
In this section, we compare the performance differences of distributed machine learning with SSP (with the different stale threshold) and DSP to verify the dynamics of the fault tolerance. For SSP, the stale threshold \(s\) is set to 2, 3 and 4, respectively. For DSP, the stale threshold \(s\) is dynamically adjusted according to the performance of each worker node, and the weak threshold \(w\) is set to 1. The experimental results are shown in Figures 13 and 14.  
Figure 13 shows the comparison of the effects of SSP with different stale thresholds and DSP on the accuracy of the distributed machine learning model. It can be seen from Figure 13 that the accuracy of the distributed machine learning model using SSP decreases with the increasing number of computation processes, but it fluctuates. This is due to the fact that the stale threshold \(s\) set by SSP cannot be increased or decreased when the performance of the computing node is changed, according to the analysis of Section 3.2.1.  
Figure 13: This figure compares the effects of SSP with different stale thresholds and DSP on the accuracy of the distributed machine learning model.  
Hence, when the performance of worker nodes tends to be similar, there will be too many training iterations as s is too large and the training results may fall into a local optimal solution, and the accuracy decreases rapidly. When the difference in worker nodes' performance increases, the performance of each computing node cannot be fully utilized since s is too small. With the increase of s, it will be more and more difficult to deal with the similar performance of worker nodes, which leads to the decrease of the accuracy. The distributed machine learning model using DSP guarantees the dynamics of the fault tolerance, and uses the performance monitoring model which evaluates the performance difference factors of each worker node to dynamically adjust the stale threshold \(s\). Therefore, regardless of whether the number of computation processes increases, the accuracy of distributed machine learning model is maintained. Figure 14 compares the difference of training time between the distributed machine learning model using SSP with different stale thresholds and DSP. The data shows that the training time of the distributed machine learning model using SSP is faster than that of the distributed machine learning model using DSP. This is because SSP ignores the finiteness of the fault tolerance and discards the accuracy to speed up the training time. In order to ensure the accuracy of the model, the distributed machine learning model using DSP sets the weak threshold \(w\), which increases the synchronization overhead. Therefore, the training time is longer than that of the distributed machine learning model using SSP, but the training time difference is very small. Using the DEFT, the distributed machine learning model using DSP achieves both high accuracy and satisfactory training time. In this section, we have evaluated the dynamics of the fault tolerance by experiments, solved the problem that SSP has low efficiency when the computing performance of the worker nodes changes dynamically.