## 2 Federated Learning  
FL is a novel AI paradigm developed by Google in 2016 [26; 42; 43]. Multiple users work together to create a global learning model in FL, eliminating the requirement for data gathering and exchange with a central server. As a result, FL is a potential privacy-preserving solution that has applications in a variety of technical fields and issues, including healthcare [44], intelligent radio access networks [44], IoT intrusion detection [45], and industrial IoT [46]. On the other hand, the traditional FL technique necessitates the presence of a central server, such as an MEC at the network edge [42]. As a result, accomplishing FL activities may be challenging, especially if the terrestrial communication infrastructure is down.  
The FL concept has been widely developed, implemented, and utilized in various domains [25]. With the rise of big data [47] and cloud computing, most large-scale research has used distributed learning [48]. FL entails creating an ML model with data located at numerous sources, without the need of sharing data. The issue of data privacy is thereby resolved. This has proven a viable answer to this problem since it maintains user privacy while exchanging information and expertise [49]. Smart behaviors, for example, are presently available on mobile devices. Image classification is used by mobile phones and tablets to anticipate the categorization of images that have been evaluated several times [50]. FL is built on improving user experience through data and information processing. Furthermore, some insurance businesses are concerned about their data security, which they refuse to share with third parties [51]. Multiparty data may be used in the FL framework in this scenario since it overcomes the ML privacy challenge. Recent FL research advances have focused on statistical difficulties [49] and security concerns [52; 53; 54]. Data interaction among scattered mobile users, imbalanced data distribution, and communication costs in equipment reliability are all elements in this process. It can motivate researchers to keep overcoming problems such as data privacy, computing restrictions, and communication costs. In addition, the FL idea has been expanded to encompass additional cross-organizational collaborative learning programs.  
The FL approach allows ML and DL models to be trained collaboratively for edge network optimization, although the performance of a complex edge network consisting of diverse devices with varied restrictions might be affected, which creates a difficulty in this area. As a result, research is underway to develop new frameworks and methodologies for FL. The authors of [55] provided an overview of FL and its applications in many fields in edge networks. The study in [56] used the Stackelberg game to investigate the inefficiency in model update transfer. In addition, the authors in [57] designed a learning-based incentive system for FL using a Stackelberg game formulation and Deep Reinforcement Learning (DRL).  
FL solves fundamental issues such as access rights, access to heterogeneous data, privacy, and security by allowing several nodes to create a cooperative learning model without sharing data samples [58]. Traffic prediction and monitoring [59], healthcare [60], communication, IoT [61], transportation and autonomous vehicles [62; 63], pharmaceutics, and medical AI are just a few of the areas where this distributed learning technology is being used. Furthermore, because FL users such as IoT devices and small sensors may not have adequate power budgets to engage actively in the FL procedure, it is vital to provide sustainable and energy-efficient solutions to improve the performance of FL-enabled networks. For instance, in [64], an iterative approach for energy efficiency in FL is developed, with closed-form solutions obtained at each stage. Furthermore, in [65], developing technology in 6G wireless systems called Intelligent Reflecting Surface (IRS) is used to significantly lower FL users' energy usage compared with those who do not utilize IRS.