# Federated Learning for Edge Computing: A Survey
## 6 Challenges
### Heterogeneous Data  
With heterogeneous hardware, it is possible to talk about different types of devices that participate in the training, which can significantly affect the whole training process and the accuracy of the global model. However, there can also be a problem when it is possible to talk about heterogeneous data or Non-IID data ([PERSON] et al., 2017). The training data of each client in FL depends to a large extent on the use of specific local devices, and therefore the data distribution of connected clients may be quite different from each other. In this situation, it is equally important to solve communication and optimization problems that eliminate these differences in the data. Federated optimization has several key characteristics that distinguish it from a typical distributed optimization problem. Non-IID training data on a given client is typically based on using an edge device by a particular user. Therefore, a specific user's local dataset will not represent the population distribution. Data imbalance can be another problem. Some users may have more possible data and a more significant local data sub-set than users who will have fewer data. This can significantly affect the speed of the whole training process. There are studies that focus on non-IID data. [PERSON] et al. [153] have focused on experiments that demonstrate the extent to which non-IID data can affect the accuracy of the global model. The accuracy of the FL and the global model can be significantly reduced if the local data is of non-IID type. In some cases, the accuracy can drop by 55% in the case of neural networks and highly skewed non-IID. One of the most famous algorithms used in training is the FedAvg algorithm. However, it is known that this algorithm is not very good at dealing with non-IID data and these data can significantly affect the output of the global model when using FedAvg [154]. There are studies that try to eliminate the problems that arise with FedAvg. Based on this, a couple of new algorithms have been developed based on the FedAvg algorithm. [PERSON] et al. [155] provide a general estimation and analysis of the convergence of federated heterogeneous optimization algorithms. They provide a principled understanding of the distortion of the alignment and the slowdown in the convergence of the global model due to data consistency. As part of this research, they propose the FedNova algorithm, in which they focus on a normalized averaging method that eliminates object inconsistency while preserving fast error convergence. This algorithm solves heterogeneity problems in federated networks. The authors tried to generalize the re-parametrization that is present in FedAvg. Initial experiments of this algorithm show that it is significantly more stable and accurate under convergence management than FedAvg. Another known algorithm is also the SCAFFOLD [149]. [PERSON] et al. focus on reducing the variance from the data collection in their local updates. They demonstrate experimentally that SCAFFOLD requires significantly fewer communication rounds and is not affected by data heterogeneity or client sampling. They also bring faster convergence. There are many articles, studies and researches that focus on comparing individual algorithms, which are summarized in the Table 6.