## 2 Related Work  
To solve the straggler problem in FL, synchronous FL has been proposed to set up a deadline and discard the local models received after the deadline. Since having more clients uploading their models can potentially accelerate the training speed, [PERSON] et al. [14] proposed to select the maximum number of clients who can upload their local models before the deadline. [PERSON] et al. [21] designed a mobile device performance-disparity-aware mechanism to adaptatively adjust the deadline. On the other hand, since the latency of a client in uploading its model depends on the allocated bandwidth, wireless resource management and client selection are usually optimized jointly. For example, [PERSON] et al. [19] aimed to jointly optimize the bandwidth allocation and client selection such that the weighted number of the selected clients is maximized. Given a set of selected clients, [PERSON] et al. [20] proposed that FedProx assign more local epochs to clients with high computing capacity, where more local epochs imply a higher computing workload. FedProx can potentially reduce the number of global iterations but may hurt the global model convergence owing to local model overfitting, especially when a data sample distribution is non-independent and identically distributed (non-IID) [22]. FedProx adds a proximal term in the loss function for each client's local model to mitigate the local model overfitting problem. [PERSON] et al. [12] considered time division multiple access (TDMA) as the wireless resource-sharing approach in wireless FL. While TDMA can enhance bandwidth utilization, it can also lead to a waiting period for the selected client to upload their local model due to the wireless channel's limited availability. To account for the potential impact of waiting time on client selection, a heuristic algorithm is developed to maximize the number of selected clients who can upload their models before the deadline. Selecting more clients to participate in each global iteration leads to the high energy consumption of the clients [23], and so [PERSON] et al. [24] designed a solution to maximize the tradeoff between the total energy consumption of the selected clients and the number of the selected clients in each global iteration. Instead of maximizing the number of selected clients in each global iteration, [PERSON] and [PERSON] [25] proposed that increasing the number of the selected clients over global iterations would have a faster convergence rate. Based on this hypothesis, they designed a long-term client selection and bandwidth allocation solution, which aims to maximize the weighted total number of the selected clients for all the global iterations while satisfying the energy budget of the clients and bandwidth capacity limitation. Here, the weight is increasing over the global iterations. Instead of waiting for the deadline to expire before updating the global model in synchronous FL, asynchronous FL allows the FL server to immediately update the global model once it receives a local model [15; 26; 27]. However, as we mentioned before, asynchronous FL has the model stale problem, i.e., the slower clients may train their local models based on an outdated global model, which results in slow convergence or even leads to model divergence [28; 29]. To mitigate the model stale problem, [PERSON] et al. [30] proposed a new global model aggregation, where the new global model equals the weighted sum of the old global model and the received local model. Here, the weight of a local model is a function of the local model staleness, i.e., a longer delay of training and uploading a local model for a client leads to a higher staleness of the local model and a lower weight of the local model. [PERSON] et al. [31] proposed the asynchronous online FL method, where different clients collaboratively train a global model over continuous data streams locally. The FL server updates the global model once it receives the gradient, learning rate, and the number of data samples from a client.