# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 1 Introduction  
With the rapid development of artificial intelligence (AI), massive intelligent devices have been deployed in the wireless internet of things (IoT), which makes the ability of providing intelligent services one of the most important evolutionary directions for future IoT. As a basic element of AI, machine learning (ML) [1] is widely utilized by various intelligent applications. However, the centralized training manner in ML becomes more and more inefficient and data transmission and privacy problems arise because massive amounts of training data are generated and stored in various intelligent devices other than the central server. Federated learning (FL) [2; 3] is a desired solution to handle this mismatch. As a distributed ML, FL only requires the transmission of model parameters other than the data themselves between client and central server, which largely reduces the amount of data transmission. Meanwhile, the user's privacy is also well protected, since the data avoid being transmitted to the central server. Despite the advantage of FL, the communication issue should be addressed when deploying it in wireless IoT networks [4]. Specifically, because of the fading effects of the wireless channel, the transmission of model parameters may experience an outage, which degenerates the performance of FL [5]. Towards this issue, an admission control algorithm is proposed in [6], where the number of accessed devices is considered as the optimization target. Furthermore, in [7], the quality of local training and the channel state are utilized to decide the accessed devices. In addition to admission control, the wireless resource optimization is also efficient for FL-aided IoT networks. For example, in [8], a jointoptimization algorithm is proposed to improve the communication reliability of FL-aided wireless networks, where the client selection and power allocation are employed to reduce the loss of the trained model. In [9], split learning and FL are combined to handle the diversity of clients with different channel states and computational capabilities. In this algorithm, client selection is achieved by a multi-arm bandit scheme, which employs both the channel states and local model as the optimized objective. In [10], a blockchain-based FL is proposed for wireless computing power networks, where the client selection is achieved by a evolutionary game-based incentive scheme. The incentive function takes the resource and security into account; however, the amount of local training data is not involved. Additionally targeting wireless computing power networks, a resource-aware FL is proposed in [11] that aims to reduce the energy consumption. The algorithm is employed to adjust the depth of the neural network and total training round without involving power and wireless channel selection. To handle the dynamics of wireless channels and network resources, in [12], the global FL models received in previous training are reused to replace erroneous local models. In this algorithm, the client selection is adopted by minimizing the accuracy loss in training data, so it focuses on the repair other than selecting superior wireless channels. All these previous works adopt the synchronous model; conversely, in [13], an asynchronous FL framework with client selection is proposed. In their optimization, the client availability and long-term fairness are taken into consideration to minimize latency. Lyapunov optimization is employed to tackle the asynchronous problem in an online manner. However, the amount of local training data is not considered in client selection. In [14], the training data of local model and wireless channel quality are jointly considered for asynchronous FL. The optimization objective is to reduce the variance and bias of the aggregated model updates, while the amount of local training data is also not involved. In [15], a joint optimization of bandwidth allocation and client scheduling is considered to achieve the ideal trade-off between training accuracy and latency.