# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 6 Results
### Federated Learning Performance  
The global model of the federated learning system, even under faulty conditions, performs robustly without any significant adverse effects. Figures 6 and 7 show the training and testing accuracy of the global model for each iteration under healthy and faulty conditions for the workloads discussed earlier. It is evident that faults in the communication microservice do not adversely affect the global model's performance, demonstrating its robust machine learning operation. Table 5 details the accuracy achieved for different numbers of users under both healthy and faulty conditions, following 100 iterations of training. These results corroborate the expected performance and uninterrupted convergence of the simple Federated Averaging algorithm, even amidst faults. Minor variations in training and testing accuracies (such as in the CIFAR-10 workload with 500 clients) are expected due to random dataset sampling.  
For an optimal machine learning performance, a robust and fault-tolerant federated learning system is necessary, particularly when handling many clients and large models.  
In the MNIST workload, an increase in the number of users from 100 to 1000 induces a minor change in training accuracy (\(\sim\)2%) and testing accuracy (\(\sim\)4%). However, this impact is amplified for CIFAR-10, resulting in a substantial \(\sim\)25% decrease in training and testing accuracy, which is attributed to larger model parameters. Enhanced model performance can be achieved by extending federated aggregation to more iterations and prolonging training durations [18, 19], albeit raising the risk of faults. This observation underscores the need for a robust, persistent federated learning platform. Such a platform should be reliable, fault-tolerant, and capable of integrating an increasing number of users, allowing them to contribute to the model and improve its performance.  
The proposed Micro-FL platform maintains consistent execution times, with minor fluctuations even under faults, demonstrating its robustness and fault tolerance. Table 6 provides the execution times for the same workload and healthy and faulty scenarios. From these data, it can be observed that for the MNIST dataset, there is a minimal variation of less than 3.5% in the execution time of the experiment. This variation occurred during the experiments with 100 and 500 users on the MNIST dataset, but these fluctuations can be deemed negligible given the short experiment durations and fluctuations in the cloud infrastructure. For more extensive federated training procedures, such variations become even less pronounced. For example, with the CIFAR-10 dataset and 1000 users, the discrepancy between execution times under healthy and faulty conditions is a mere 0.06%. This observation highlights that the proposed Micro-FL platform and its fault-tolerant design do not allow faults to influence the execution time. Therefore, the federated learning process does not experience delays due to faults.  
Figure 6: Global model’s training and testing accuracies on the MNIST dataset across 100 iterations of training. (**a**) MNIST in the healthy operation scenario. (**b**) MNIST in the faulty operation scenario.  
Figure 7: Global model’s training and testing accuracies on the CIFAR-10 dataset across 100 iterations of training. (**a**) CIFAR-10 in the healthy operation scenario. (**b**) CIFAR-10 in the faulty operation scenario.