## 4 Challenges of Federated Learning
In the end, this framework efficiently improves the applicability of FL to numerous heterogeneous contexts while offering good convergence speed, accuracy, and computation/communication efficiency. Moreover, to address the heterogeneity of computational capabilities of edge devices, [PERSON], [PERSON], et al. [75] suggested a novel heterogeneous FL framework built on multi-branch deep neural network models. This framework enables the client devices to choose the best sub-branch model for their computing capabilities. They also provide MFedAvg, a model training aggregation approach that uses branch-wise averaging-based aggregation. Extensive experiments on MNIST, FashionMNIST, MedMNIST, and CIFAR-10 demonstrate that their suggested approaches can achieve satisfactory performance with guaranteed convergence and efficiently use all the resources available for training across different devices with lower communication cost than its homogeneous counterpart. [PERSON], [PERSON], and [PERSON], [PERSON] [73] also concentrate on reducing the degree of device heterogeneity by suggesting AQFL, a straightforward and useful method that uses adaptive model quantization to homogenize the customers' computational resources. They assess AQFL using five standard FL metrics. The findings demonstrate that AQFL achieves roughly the same quality and fairness in diverse environments as the model trained in homogeneous conditions. * Deep learning model architecture: DNN is proven to outperform other models in previous research, but its architecture in both traditional ML methods and FL was often pre-fixed [44; 76]. Such a pre-defined setting carries many subjective factors that lead to the consequence that the models may fall into local optimal states. To address this challenge, [77] proposed a unified deep learning framework that suits data in multiple modalities and dimensions; the authors proved that using the proposed framework yields better performance than traditional ML approaches. In addition, [44] recommended using neural architecture search (NAS) to find the appropriate deep learning architecture for each task on each client. * Securing FL system: Clients can learn a common global model cooperatively using FL without disclosing their training data to a cloud server. Malicious clients, however, can tamper with the global model and cause it to forecast inaccurate labels for test cases. Through ensemble, FL, [PERSON], [PERSON], et al. [78] close this gap. They utilize the technique to train numerous global models, each of which is learned using a randomly chosen subset of clients, given any basic FL algorithm. They use the global models' majority vote when predicting a testing example's label. They demonstrate that their ensemble FL is demonstrably safe against malicious clients using any FL base algorithm. In particular, a testing example's label predicted by their ensemble global model is provably unaffected by a finite number of malevolent clients. They also demonstrate the tightness of their derived limit. On the MNIST and Human Activity Recognition datasets, they assess their methodology. For instance, when 20 out of 1000 clients are malicious, their technique may obtain a verified accuracy of 88% on MNIST.