# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 4 Experiments and Results
### The Finite of the Fault Tolerance  
In this section, we compare the performance differences of distributed machine learning with three different parameter communication optimization strategies (including BSP, SSP and DSP) to verify the finite nature of the fault tolerance. We use the three nodes in the cluster to train the distributed machine learning model, where one node acts as a parameter server and two nodes act as worker nodes. By comparing the training time and accuracy of the machine learning model under the different communication optimization strategies, we verify the limitation of the fault tolerance of the iterative learning convergence algorithm, and evaluate the performance of DSP. The experimental results are shown in Figures 11 and 12, where the stale threshold \(s\) of SSP and DSP is set to 3, the weak threshold \(w\) of DSP is set to 1.  
Figure 11 compares the accuracy of the machine learning model when applying different parameter communication optimization strategies. As shown in the figure, regardless of which parameter communication optimization strategy is used for distributed machine learning model  
Figure 11: This figure compares the accuracy of the model for machine learning with each parameter communication optimization strategy.  
Figure 12: This figure compares the training time of machine learning model training with each parameter communication optimization strategy.  
training, the accuracy decreases as the computation processes increase. Figure 12 compares the training time of machine learning model training with each parameter communication optimization strategy. Similarly, regardless of which parameter communication optimization strategy is used, the training time decreases with the increase of computation processes. With the gradual increase of computation processes, the number of communications between worker nodes and PS will be increased. Therefore, when the number of computation processes reaches a certain value, the communication cost is greater than the computing cost, and the training time is no longer reduced. BSP is required to enter the synchronization barrier after each iteration to ensure strong consistency and accuracy, but it also takes a lot of time. SSP leverages the fault tolerance, and the training time is lower, but it is not aware of the finiteness of the fault tolerance, and does not set the weak threshold \(w\), which leads to the low accuracy as the increasing of compute processes. DSP in this experiment does not use the performance monitoring module and cannot dynamically adjust the delay threshold \(s\), but it uses the finiteness of the fault tolerance, and sets the weak threshold \(w\), so there is still a good accuracy guaranteed with the increasing of compute processes, the training time is lower than that of BSP and close to that of SSP. In this section, we have experimentally evaluated the finiteness of the fault tolerance of the machine learning iteration-convergence algorithm, solved the problem that SSP has the low efficiency for the distributed machine learning algorithm in clusters that is composed of nodes with similar performance.