## 4 Challenges of Federated Learning  
* Communication cost: Because federated networks may have a vast number of devices, the computation time of FL may take longer than local training. Even though there have been several studies addressing this issue, it is imperative to build communication-efficient approaches as a result of increasing clients. Some crucial factors to consider in this situation are reducing the total number of communication rounds, finding fast convergence algorithms, and making as small model updates as possible at each round. To address this issue, adding split learning to FL is one of the effective solutions. The following are the two examples of this direction. [PERSON], [PERSON],et al. [69] first examine various earlier methods for federated optimization and demonstrate that, even in straightforward convex settings with deterministic updates, their fixed points do not always have to coincide with the stationary pointsof the original optimization problem. They provide FedSplit, a set of methods for solving distributed convex minimization with an additive structure that is based on operator splitting processes, to address these problems. They demonstrate that these methods have the right fixed points, which correspond to the solutions of the initial optimization problem, and they describe the speeds at which these methods converge under various conditions. These techniques are resistant to the inaccurate calculation of intermediate local quantities, according to their theory. They support their argument with a few straightforward experiments that show how effective their plans are in action. In addition, [PERSON], [PERSON], et al. proposed SplitFed [70], a novel distributed ML approach called splitfed learning (SFL) that combines (FL) and split learning (SL), eliminating the inherent drawbacks of each approach, as well as a refined architectural configuration incorporating differential privacy and PixelDP to improve data privacy and model robustness. According to their study and actual findings, SFL offers comparable test accuracy and communication effectiveness to SL while having a much shorter calculation time per global epoch for numerous clients. The effectiveness of its communication through FL also increases with the number of clients, just like in SL. Additionally, the effectiveness of SFL with privacy and resilience safeguards is assessed in more extensive experimental situations. * Heterogeneity: FL faces a considerable challenge when operating in various devices and data of the whole system [71; 72; 73]. Indeed, increasingly intelligent devices can connect to train the FL system. These devices have different hardware and software architectures (e.g., storage capacity, CPU topologies, power consumption level, operating system software, network bandwidth). In addition, each type of device is also designed to collect or generate different amounts and types of data, thus data nowadays is usually not independent and identically distributed (non IID data). To accommodate the heterogeneity of the system, it is necessary to develop clients' selection solutions during the training process to ensure convergence of the whole system. Dealing with the diversity in statistical heterogeneity, multi-model FL should be considered [44]. [PERSON], [PERSON], et al. [74] created a solid structure-information alignment across collaborative models. The authors offer a unique FL approach to address the heterogeneity of the FL system. To provide explicit feature information allocation in various neural network topologies, they specifically construct a feature-oriented regulation mechanism (Net). Matchable structures with similar feature information may be initialized at the very early training stage by using this regulating strategy on collaborative models. Dedicated cooperation methods further provide ordered information delivery with clear structure matching and full model alignment during the FL process in both IID and non-IID scenarios.